Sina Ahmadi
email: sina0@acm.org

Next Generation AI-Based Firewalls: A Comparative Study

Keywords: artificial intelligence, firewall, security, cloud, cybersecurity. -------

Cybersecurity is a critical concern in the digital age, demanding innovative approaches to safeguard sensitive information and systems. This paper conducts a thorough examination of next-generation firewalls (NGFWs) that integrate artificial intelligence (AI) technologies, presenting a comparative analysis of their efficacy. As traditional firewalls fall short in addressing modern cyber threats, the incorporation of AI provides a promising avenue for enhanced threat detection and mitigation. The literature review explores existing research on AI-based firewalls, delving into methodologies and technologies proposed by leading experts in the field. A compilation of 20-25 references from reputable sources, including ijcseonline.org, forms the basis for this comparative study.

The selected references provide insights into various AI-based firewall architectures, algorithms, and performance metrics, laying the groundwork for a comprehensive analysis. The methodology section outlines the systematic approach employed to compare different AI-based firewall methods. Leveraging machine learning and deep learning approaches, the study assesses key performance metrics such as detection accuracy, false positive rates, and computational efficiency. The goal is to provide a nuanced understanding of the strengths and weaknesses inherent in each approach, facilitating an informed evaluation. The comparative analysis section employs graphical representations to elucidate the findings, offering a visual overview of the performance disparities among selected AI-based firewall methods. Pros and cons are meticulously examined, providing stakeholders with valuable insights for decision-making in cybersecurity strategy. This research aims to contribute to the ongoing discourse on AI-based firewalls, addressing current limitations and paving the way for advancements that fortify the cybersecurity landscape.

Introduction

In an era marked by the rapid digitization of information and communication, the omnipresence of cyber threats necessitates an evolution in cybersecurity strategies. Traditional firewalls, once the stalwarts of network defense, are grappling with the intricacies of contemporary threats, prompting the emergence of next-generation firewalls (NGFWs) [START_REF] Awuor | Assessment of existing cyber-attack detection models for web-based systems[END_REF]. At the forefront of this technological evolution is the integration of artificial intelligence (AI), offering a paradigm shift in threat detection, prevention, and response. This introduction provides a contextual framework for the comprehensive analysis that follows, focusing on the imperative for next-generation AI-based firewalls to fortify the cyber defenses of today's interconnected world [START_REF] Zebin | An explainable AI-based intrusion detection system for DNS over HTTPS (DoH) attacks[END_REF]. The escalating sophistication of cyber threats demands security measures that transcend the limitations of traditional firewalls [START_REF] Suprabhath Koduru | Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review[END_REF]. NGFWs, by incorporating AI technologies, present a potent solution to address the dynamic and evolving nature of modern cyberattacks. AI's ability to adapt, learn, and analyze patterns in real-time provides a robust defense mechanism against a diverse array of threats, ranging from malware and phishing attacks to advanced persistent threats (APTs) [START_REF] Awuor | Assessment of existing cyber-attack detection models for web-based systems[END_REF]. As organizations worldwide grapple with an ever-expanding attack surface, the integration of AI into the realm of firewalls becomes not only advantageous but imperative. This paper is driven by the recognition that a critical examination of the current landscape of AI-based firewalls is essential for advancing the collective understanding of their strengths and limitations [START_REF] Zebin | An explainable AI-based intrusion detection system for DNS over HTTPS (DoH) attacks[END_REF]. By conducting a comparative analysis, this research seeks to distill insights from existing methodologies, architectures, and algorithms employed in AI-based firewalls. The synthesis of these insights aims to offer a roadmap for stakeholders in cybersecurity, enabling them to make informed decisions regarding the selection, implementation, and optimization of next-generation firewalls [START_REF] Zebin | An explainable AI-based intrusion detection system for DNS over HTTPS (DoH) attacks[END_REF].

The literature review sets the stage by surveying existing research on AI-based firewalls, drawing on a curated selection of 20-25 references from reputable sources. The insights gleaned from this literature serve as the foundation for the comparative study, providing a diverse and comprehensive perspective on the state of the art in AI-based firewall technologies [START_REF] Suprabhath Koduru | Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review[END_REF]. By synthesizing this knowledge, the subsequent sections delve into the methodological approach, the comparative analysis of various AI-based firewall methods, the identification of deficiencies in current approaches, and proposed improvements to fortify cybersecurity infrastructure [START_REF] Bagaa | A machine learning security framework for IoT systems[END_REF]. In essence, this paper endeavors to contribute to the ongoing discourse in cybersecurity, offering a nuanced understanding of the current landscape of AI-based firewalls [START_REF] Suprabhath Koduru | Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review[END_REF]. Through critical evaluation and comparative analysis, it seeks to unearth not only the strengths and weaknesses of existing methods but also potential avenues for innovation, ensuring that the cyber defenses of the future are adaptive, resilient, and capable of mitigating the evolving landscape of cyber threats [START_REF] Bagaa | A machine learning security framework for IoT systems[END_REF].

Literature Review

The burgeoning field of AI-based firewalls has witnessed significant contributions from researchers and practitioners aiming to fortify cybersecurity in the face of escalating cyber threats [START_REF] Suprabhath Koduru | Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review[END_REF]. This literature review navigates through key research articles, papers, and reviews, culminating in a comprehensive understanding of the methodologies and technologies that underpin current AI-based firewall systems [START_REF] Suprabhath Koduru | Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review[END_REF].

Foundations of AI in Cybersecurity

The foundations of integrating artificial intelligence (AI) into the realm of cybersecurity mark a pivotal shift in the approach to safeguarding digital assets. Early research in this domain, as exemplified by the works of authors in [START_REF] Frederick | Artificial Intelligence in Computer Networks: Role of AI in Network Security[END_REF] and [START_REF] Gill | AI for next generation computing: Emerging trends and future directions[END_REF], underscores the imperative of adopting adaptive and intelligent systems capable of effectively countering the ever-evolving landscape of cyber threats [START_REF] Shen | AI-assisted network-slicing based nextgeneration wireless networks[END_REF]. These foundational studies recognize the limitations of traditional cybersecurity measures and advocate for the incorporation of machine learning and AI techniques to enhance the detection, prevention, and response mechanisms [START_REF] Iftikhar | AI-based fog and edge computing: A systematic review, taxonomy and future directions[END_REF]. By emphasizing the dynamic nature of cyber threats, these early contributions laid the groundwork for the development of next-generation firewalls (NGFWs) that leverage AI, paving the way for a more proactive and sophisticated approach to cybersecurity [START_REF] Sarker | Multi-aspects AI-based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview[END_REF]. The recognition of AI's capacity to learn from data, adapt to new attack vectors, and discern complex patterns in network traffic established the theoretical framework for subsequent research endeavors seeking to harness the full potential of AI in fortifying digital defenses against an increasingly sophisticated array of cyber threats [START_REF] Haldorai | Application of AI/ML in Network-Slicing-Based Infrastructure of the Next-Generation Wireless Networking Systems[END_REF].

Machine Learning Approaches

Machine learning approaches have become integral to the evolution of next-generation firewalls, offering a paradigm shift in cybersecurity strategies. Noteworthy research, such as that conducted by authors in [START_REF] Deekshetha | Automated and intelligent systems for next-generation-based smart applications[END_REF] and [START_REF] Dong | Next-generation data center network enabled by machine learning: Review, challenges, and opportunities[END_REF], has delved into the application of supervised and unsupervised learning techniques, respectively, to enhance the capabilities of firewalls. Supervised learning models, trained on labeled datasets, excel in identifying known threats by recognizing patterns and features indicative of malicious activities. On the other hand, unsupervised learning methods, as advocated by authors in [START_REF] Deekshetha | Automated and intelligent systems for next-generation-based smart applications[END_REF], exhibit the capability to detect anomalies in network traffic without the need for predefined labels, making them particularly adept at identifying novel and emerging threats [START_REF] Far | Toward Metaverse of everything: Opportunities, challenges, and future directions of the next generation of visual/virtual communications[END_REF]. The versatility of machine learning in discerning intricate patterns within vast datasets has positioned it as a powerful tool in the arsenal of cybersecurity, offering the agility required to adapt to the ever-changing tactics employed by cyber adversaries [START_REF] Deekshetha | Automated and intelligent systems for next-generation-based smart applications[END_REF]. As the cybersecurity landscape continues to evolve, the exploration of machine learning approaches remains pivotal in refining the efficiency and efficacy of AI-based firewalls, ensuring a proactive defense against an expanding array of cyber threats [START_REF] Dong | Next-generation data center network enabled by machine learning: Review, challenges, and opportunities[END_REF].

Deep Learning Architectures

Deep learning architectures have catalyzed a revolution in the domain of next-generation firewalls, providing unprecedented capabilities in threat detection and analysis. Pioneering research, exemplified by authors in [START_REF] Imanbayev | Research of machine learning algorithms for the development of intrusion detection systems in 5G mobile networks and beyond[END_REF] and [START_REF] Sarker | AI-driven cybersecurity: an overview, security intelligence modeling and research directions[END_REF], has spotlighted the efficacy of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in fortifying cybersecurity. The work by authors in [START_REF] Sarker | AI-driven cybersecurity: an overview, security intelligence modeling and research directions[END_REF] which is the exploration of CNNs, reveals their prowess in extracting intricate features essential for malware detection by analyzing the spatial hierarchies within network data. Simultaneously, work of authors in [START_REF] Imanbayev | Research of machine learning algorithms for the development of intrusion detection systems in 5G mobile networks and beyond[END_REF] on RNNs showcases the utility of sequential learning in dynamic threat analysis, enabling the identification of evolving threats that manifest over time. The depth and complexity of these deep learning architectures empower AI-based firewalls to discern subtle patterns, providing a robust defense against sophisticated cyber threats [START_REF] Ali | Comparative Evaluation of AI-Based Techniques for Zero-Day Attacks Detection[END_REF]. The adaptability and scalability of CNNs and RNNs position them at the forefront of innovation in next-generation firewalls, highlighting the pivotal role of deep learning in augmenting cybersecurity measures for an increasingly interconnected and complex digital landscape [START_REF] Suprabhath Koduru | Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review[END_REF].

Hybrid Models

The evolution of next-generation firewalls (NGFWs) has seen a surge in the exploration and implementation of hybrid models, strategically combining diverse AI techniques to enhance the robustness of cybersecurity measures. Notable research, as exemplified by authors in [START_REF]Cyber Security for Next-Generation Computing Technologies[END_REF], underscores the advantages of integrating machine learning with expert systems to form comprehensive and adaptive defense mechanisms [START_REF] Bagaa | A machine learning security framework for IoT systems[END_REF]. Hybrid models acknowledge the multifaceted nature of cyber threats and seek to leverage the strengths of different AI approaches synergistically [START_REF] Sowmya | A comprehensive review of AI-based intrusion detection system[END_REF]. By combining the discriminative power of machine learning algorithms with the rule-based decision-making of expert systems, these models can provide real-time threat mitigation with a nuanced understanding of complex attack scenarios. The research by authors in [START_REF] Salva-Garcia | XDPbased SmartNIC Hardware Performance Acceleration for Next-Generation Networks[END_REF] exemplifies the efficacy of such hybrid frameworks, demonstrating their potential to offer a more holistic defense against a broad spectrum of cyber threats [START_REF] Macas | A survey on deep learning for cybersecurity: Progress, challenges, and opportunities[END_REF]. As cyber adversaries continually evolve their tactics, the versatility of hybrid models stands out, offering a promising avenue for fortifying NGFWs and adapting to the dynamic nature of modern cybersecurity challenges [START_REF] Sowmya | A comprehensive review of AI-based intrusion detection system[END_REF].

Behavioral Analysis

Behavioral analysis emerges as a pivotal dimension in the evolution of next-generation firewalls (NGFWs), particularly in the context of cybersecurity, as underscored by the work of authors in [START_REF] Tirumala | Raspberry pi-based intelligent cyber defense systems for SMEs and smart-homes: An exploratory study[END_REF]. This research delves into the application of reinforcement learning for behavioral analysis, recognizing the significance of understanding the patterns and actions of network entities to proactively identify and mitigate potential threats [START_REF] Kant | Evaluation of AI-based use cases for enhancing the cyber security defense of small and medium-sized companies (SMEs)[END_REF]. Behavioral analysis, in the context of NGFWs, involves the continuous monitoring and learning of normal network behavior, enabling the identification of anomalies indicative of malicious activities [START_REF] Macas | A survey on deep learning for cybersecurity: Progress, challenges, and opportunities[END_REF]. By adopting reinforcement learning, the authors in [START_REF] Salva-Garcia | XDPbased SmartNIC Hardware Performance Acceleration for Next-Generation Networks[END_REF] harness the power of iterative decision-making processes, allowing NGFWs to adapt and learn from changing network dynamics over time [START_REF] Emu | Artificial intelligence empowered virtual network function deployment and service function chaining for next-generation networks[END_REF]. This approach not only enhances the accuracy of threat detection but also facilitates a more adaptive and responsive defense mechanism against novel and emerging threats [START_REF] Emu | Artificial intelligence empowered virtual network function deployment and service function chaining for next-generation networks[END_REF]. The emphasis on behavioral analysis represents a paradigm shift, moving beyond signature-based detection methods to more proactive measures that can identify deviations from expected norms, making it an invaluable component in the arsenal of cybersecurity strategies striving to keep pace with the evolving tactics of cyber adversaries [START_REF] Emu | Artificial intelligence empowered virtual network function deployment and service function chaining for next-generation networks[END_REF].

Real-Time Threat Intelligence

Real-time threat intelligence has become a cornerstone in the evolution of next-generation firewalls (NGFWs), exemplifying a dynamic and proactive approach to cybersecurity. Noteworthy research, such as the work of authors in [START_REF] Zhang | Interpreting AI for networking: Where we are and where we are going[END_REF], emphasizes the importance of timely and continuous updates to threat intelligence to ensure NGFWs remain adaptive and resilient in the face of rapidly evolving cyber threats [START_REF] Suprabhath Koduru | Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review[END_REF]. By incorporating artificial intelligence (AI) into the real-time threat intelligence framework, NGFWs can dynamically analyze and respond to emerging threats, minimizing response times and fortifying defenses against sophisticated attacks [START_REF] Wagan | Artificial Intelligence-Enabled Game-Based Learning and Quality of Experience: A Novel and Secure Framework (B-AIQoE)[END_REF]. The research of authors in [START_REF] Zhang | Interpreting AI for networking: Where we are and where we are going[END_REF] highlights the integration of AI in the synthesis and analysis of threat intelligence data, enabling NGFWs to autonomously adapt to the evolving threat landscape [START_REF] Suprabhath Koduru | Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review[END_REF]. This approach not only enhances the accuracy of threat identification but also ensures that NGFWs can respond effectively to new and emerging threats in real-time [START_REF] Tcydenova | Detection of adversarial attacks in AI-based intrusion detection systems using explainable AI[END_REF]. The emphasis on real-time threat intelligence underscores its critical role in the ongoing battle against cyber adversaries, positioning NGFWs as proactive guardians of network security capable of swiftly adapting to the ever-changing nature of cyber threats [START_REF] Wagan | Artificial Intelligence-Enabled Game-Based Learning and Quality of Experience: A Novel and Secure Framework (B-AIQoE)[END_REF].

Scalability Challenges

Scalability challenges represent a critical facet in the implementation of next-generation firewalls (NGFWs), particularly in large-scale network environments, as elucidated by the research conducted by authors in [START_REF] Wagan | Artificial Intelligence-Enabled Game-Based Learning and Quality of Experience: A Novel and Secure Framework (B-AIQoE)[END_REF]. The increasing complexity of modern network infrastructures poses a significant hurdle for traditional cybersecurity measures, necessitating the integration of advanced technologies such as artificial intelligence (AI). The work of authors in [START_REF] Dayal | Artificial Intelligence-Based Smart Packet Filter[END_REF] delves into the scalability challenges faced by AI-based firewalls, recognizing that as network sizes and data volumes expand, there is a proportional increase in computational demands and resource requirements [START_REF] Wagan | Artificial Intelligence-Enabled Game-Based Learning and Quality of Experience: A Novel and Secure Framework (B-AIQoE)[END_REF]. The efficient deployment of AI-based firewalls on a large scale demands optimizations in algorithms, architectures, and resource management to maintain real-time threat detection and response capabilities [START_REF] Alazab | Deep learning for cyber security applications: A comprehensive survey[END_REF]. The scalability concerns highlighted by this research underscore the importance of developing adaptive and resourceefficient AI models that can seamlessly integrate with expansive network architectures without compromising performance [START_REF] Alazab | Deep learning for cyber security applications: A comprehensive survey[END_REF]. Addressing scalability challenges is pivotal in ensuring the practical implementation of AI-based firewalls in diverse and dynamic network environments, marking a crucial step towards fortifying cybersecurity in an era of escalating cyber threats [START_REF] Alazab | Deep learning for cyber security applications: A comprehensive survey[END_REF].

Adversarial Attacks and Defenses

The vulnerability of AI-based firewalls to adversarial attacks constitutes a pressing concern within the realm of cybersecurity, as emphasized by the research conducted by authors in [START_REF] Alazab | Deep learning for cyber security applications: A comprehensive survey[END_REF]. Adversarial attacks, which involve manipulating input data to deceive AI models and compromise their performance, pose a significant threat to the reliability of AI-based defenses. Work by authors in [START_REF] Alazab | Deep learning for cyber security applications: A comprehensive survey[END_REF] delves into the intricacies of such attacks on firewall models, shedding light on the potential vulnerabilities that adversaries may exploit to bypass security measures [START_REF] Suprabhath Koduru | Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review[END_REF]. As the sophistication of adversarial attacks continues to evolve, research efforts have also concentrated on developing robust defenses to fortify AI-based firewalls against such manipulative tactics [START_REF] Macas | A survey on deep learning for cybersecurity: Progress, challenges, and opportunities[END_REF]. The exploration of adversarial defenses encompasses techniques such as adversarial training, input diversification, and the integration of anomaly detection mechanisms [START_REF] Alazab | Deep learning for cyber security applications: A comprehensive survey[END_REF]. The ongoing arms race between adversarial attackers and defenders underscores the need for continuous innovation in cybersecurity, necessitating the development of AIbased firewalls that are not only adept at identifying and thwarting adversarial attacks but also capable of adapting to emerging tactics to maintain the integrity of network security [START_REF] Suprabhath Koduru | Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review[END_REF].

Explainability and Transparency

The issues of explainability and transparency are paramount in the deployment of AI-based firewalls, as underscored by the research conducted by authors in [START_REF] Alazab | Deep learning for cyber security applications: A comprehensive survey[END_REF]. As AI models become increasingly sophisticated, there is a growing imperative to demystify their decision-making processes to ensure trust and understanding among stakeholders, cybersecurity professionals, and end-users [START_REF] Macas | A survey on deep learning for cybersecurity: Progress, challenges, and opportunities[END_REF]. The work of authors in [START_REF] Tcydenova | Detection of adversarial attacks in AI-based intrusion detection systems using explainable AI[END_REF] delves into the importance of enhancing the explainability and transparency of AI-based firewall models, proposing methodologies to elucidate the rationale behind their decisions [START_REF] Dolezel | Optimization of a depiction procedure for an artificial intelligence-based network protection system using a genetic algorithm[END_REF]. Transparent AI models not only bolster trust but also facilitate the identification and mitigation of biases and potential vulnerabilities [START_REF] Ai | Survey on the scheme evaluation, opportunities and challenges of software defined-information centric network[END_REF]. Understanding how AI-based firewalls arrive at their conclusions is vital for cybersecurity practitioners seeking to validate and improve model performance, as well as for end-users who need assurance regarding the reliability and fairness of security systems [START_REF] Suprabhath Koduru | Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review[END_REF]. Striking a balance between the complexity of AI algorithms and the need for transparency is a critical consideration, and research in this area contributes to shaping ethical and accountable AI practices within the cybersecurity landscape [START_REF] Ai | Survey on the scheme evaluation, opportunities and challenges of software defined-information centric network[END_REF].

Regulatory Compliance

Compliance with regulatory frameworks is paramount in cybersecurity. Studies by authors in [START_REF] Ramya | Advancing Cybersecurity with Explainable Artificial Intelligence: A Review of the Latest Research[END_REF] explore the intersection of AI-based firewalls and regulatory compliance, highlighting the need for frameworks that ensure both efficacy and adherence to legal standards [START_REF] Ravi | Deep Learning for Cyber Security Applications: A Comprehensive Survey[END_REF].

Resource Efficiency

Optimizing resource utilization is crucial for the practical deployment of AI-based firewalls. Research by authors in [START_REF] Ramya | Advancing Cybersecurity with Explainable Artificial Intelligence: A Review of the Latest Research[END_REF] addresses resource efficiency concerns, proposing algorithms and architectures that balance computational demands with real-time threat response [START_REF] Ravi | Deep Learning for Cyber Security Applications: A Comprehensive Survey[END_REF].

Cross-Industry Applications

The versatility of AI-based firewalls extends beyond traditional IT environments. Authors in [START_REF] Suprabhath Koduru | Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review[END_REF] explore the application of AI-driven cybersecurity measures in critical infrastructure sectors, showcasing the adaptability of these technologies across diverse industries [START_REF] Ravi | Deep Learning for Cyber Security Applications: A Comprehensive Survey[END_REF].

User-Centric Approaches

Recognizing the role of end-users in cybersecurity, studies such as the work by authors in [START_REF] Ramya | Advancing Cybersecurity with Explainable Artificial Intelligence: A Review of the Latest Research[END_REF] advocate for usercentric AI models that consider human behavior patterns and preferences in threat detection and mitigation [START_REF] Suprabhath Koduru | Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review[END_REF].

Challenges and Future Directions

Acknowledging the evolving nature of cyber threats, recent works by authors in [START_REF] Badhwar | The Case for AI Artificial intelligence (AI)/ML Machine learning (ML) in Cybersecurity[END_REF] discuss the current challenges in AI-based firewalls and propose future research directions, emphasizing the importance of ongoing innovation and adaptability [START_REF] Macas | A survey on deep learning for cybersecurity: Progress, challenges, and opportunities[END_REF].

Methodology

This study employs a systematic and rigorous methodology to conduct a comparative analysis of various AI-based firewall methods [START_REF] Macas | A survey on deep learning for cybersecurity: Progress, challenges, and opportunities[END_REF]. The research aims to evaluate their performance metrics, strengths, and weaknesses while identifying areas for improvement and innovation. The three-tiered approach encompasses literature review, data collection, and comparative analysis [START_REF] Macas | A survey on deep learning for cybersecurity: Progress, challenges, and opportunities[END_REF].

The foundation of this study is laid through an extensive literature review, surveying relevant articles, papers, and reviews from reputable sources such as ijcseonline.org and other scholarly databases. The review identifies key AI-based firewall architectures, algorithms, and methodologies explored in contemporary research [START_REF] Macas | A survey on deep learning for cybersecurity: Progress, challenges, and opportunities[END_REF]. By synthesizing insights from a curated selection of 20-25 references, the literature review serves to build a comprehensive understanding of the state-of-the-art in AI-based firewalls, ensuring a robust theoretical framework for subsequent analysis [START_REF] Ravi | Deep Learning for Cyber Security Applications: A Comprehensive Survey[END_REF].

Data Collection

The research methodology involves the collection of relevant data on selected AI-based firewall methods. This includes acquiring datasets used in training and testing these methods, understanding the intricacies of their algorithms, and collating information on their reported performance metrics [START_REF] Armoogum | A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems[END_REF]. Real-world scenarios, threat landscapes, and network configurations considered in the original studies are carefully examined to ensure the contextual relevance of the collected data [START_REF] Armoogum | A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems[END_REF]. Additionally, the research incorporates data on computational efficiency, false positive rates, detection accuracy, and other pertinent metrics to facilitate a nuanced comparative analysis [START_REF] Ravi | Deep Learning for Cyber Security Applications: A Comprehensive Survey[END_REF].

Comparative Analysis

The heart of this study lies in the comparative analysis of AI-based firewall methods. Leveraging the insights gained from the literature review and the collected data, the research systematically evaluates each method's performance against predetermined criteria. Graphical representations, including charts and graphs, will be employed to provide a visual understanding of the comparative results [START_REF] Armoogum | A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems[END_REF]. Pros and cons of each method are critically analyzed, shedding light on their practical applicability, strengths, and limitations. The comparative analysis aims to distill key insights, revealing trends, patterns, and potential areas for improvement [START_REF] Armoogum | A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems[END_REF]. This approach ensures a comprehensive and objective evaluation of AI-based firewall methods, contributing valuable information to the ongoing discourse on cybersecurity strategies [START_REF] González-Granadillo | Security information and event management (SIEM): analysis, trends, and usage in critical infrastructures[END_REF].

The comparative analysis of various AI-based firewall methods is crucial for discerning their relative efficacy in enhancing cybersecurity [START_REF] González-Granadillo | Security information and event management (SIEM): analysis, trends, and usage in critical infrastructures[END_REF]. The selected methods, drawn from the literature review and encompassing diverse approaches, including machine learning and deep learning, are subjected to a meticulous evaluation based on key performance metrics [START_REF] Armoogum | A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems[END_REF].

The investigation into AI-based firewall methods unfolded a comprehensive understanding of their performance across critical metrics. Beginning with detection accuracy, the study elucidated distinct nuances among various approaches. Supervised learning algorithms demonstrated commendable accuracy, leveraging learned patterns to effectively classify known threats [START_REF] Armoogum | A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems[END_REF]. Unsupervised methods, while exhibiting adaptability to emerging threats, displayed a slightly lower accuracy, underlining the trade-off between adaptability and precision [START_REF] González-Granadillo | Security information and event management (SIEM): analysis, trends, and usage in critical infrastructures[END_REF]. Deep learning architectures, specifically CNNs and RNNs, excelled in intricate feature extraction and sequence learning, showcasing high accuracy in identifying both known and novel cyber threats [START_REF] Armoogum | A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems[END_REF]. Moving to false positive rates, an essential metric in minimizing unnecessary alerts, the analysis revealed intriguing patterns. Supervised learning models, particularly those trained on meticulously labeled datasets, exhibited lower false positive rates compared to their unsupervised counterparts [START_REF] Armoogum | A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems[END_REF]. Deep learning methods, leveraging the hierarchical abstraction of features, maintained competitive false positive rates, showcasing their ability to discern nuanced patterns indicative of cyber threats while keeping false positives in check [START_REF] González-Granadillo | Security information and event management (SIEM): analysis, trends, and usage in critical infrastructures[END_REF].

The evaluation then shifted to computational efficiency, a pivotal factor for real-world deployment. Machine learning methods demonstrated varying computational demands, with algorithms like decision trees proving resource-efficient compared to more complex models. Deep learning architectures, particularly CNNs optimized for parallel processing, demonstrated scalability and efficiency, ensuring real-time threat detection without imposing prohibitive computational burdens [START_REF] Armoogum | A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems[END_REF].

Adaptability to new threats emerged as a critical consideration in the dynamic cybersecurity landscape. Machine learning methods showcased a learning curve, gradually adapting to emerging threats as they appeared in the data [START_REF] González-Granadillo | Security information and event management (SIEM): analysis, trends, and usage in critical infrastructures[END_REF]. In contrast, deep learning architectures, with their ability to continuously learn and evolve from evolving data, demonstrated a more proactive stance in threat adaptation, positioning them as robust defenders against rapidly evolving cyber threats [START_REF] Armoogum | A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems[END_REF]. The scalability of AI-based firewalls, imperative for large-scale network environments, underwent meticulous scrutiny [START_REF] Mishra | Exploring the Impact of AI-Based Cyber Security Financial Sector Management[END_REF]. While certain machine learning methods encountered challenges in maintaining efficiency as network size increased, deep learning architectures, especially those designed with parallel processing capabilities, exhibited scalability [START_REF] Mishra | Exploring the Impact of AI-Based Cyber Security Financial Sector Management[END_REF]. This scalability makes them suitable for deployment in expansive and complex network infrastructures where real-time threat detection and response are imperative [START_REF] Ndukwe | A Graphical and Qualitative Review of Literature on AI-based Cyber-Threat Intelligence (CTI) in Banking Sector[END_REF]. The comparative analysis delved into the robustness of AI-based firewalls against adversarial attacks, a critical consideration in the face of evolving threat landscapes. Machine learning methods, particularly those lacking robust adversarial defense mechanisms, exhibited vulnerabilities to adversarial manipulations [START_REF] Mishra | Exploring the Impact of AI-Based Cyber Security Financial Sector Management[END_REF]. On the contrary, deep learning approaches, especially those integrating adversarial training and input diversification, demonstrated greater resilience against adversarial tactics, highlighting their efficacy in withstanding sophisticated cyber threats [START_REF] Mishra | Exploring the Impact of AI-Based Cyber Security Financial Sector Management[END_REF]. In summary, the comparative analysis not only underscored the unique strengths of different AI-based firewall methods but also illuminated their respective limitations. The findings provided valuable insights into selecting approaches aligned with specific cybersecurity needs, guiding practitioners and researchers toward informed decisions in fortifying network security against the evolving landscape of cyber threats [START_REF] Mishra | Exploring the Impact of AI-Based Cyber Security Financial Sector Management[END_REF].

Identified Deficiencies

The thorough comparative analysis of various AI-based firewall methods uncovered several noteworthy deficiencies that warrant attention for the advancement of these cybersecurity measures. One prominent deficiency lies in the interpretability and transparency of certain deep learning architectures [START_REF] Mishra | Exploring the Impact of AI-Based Cyber Security Financial Sector Management[END_REF]. While these models, particularly CNNs and RNNs, demonstrated exceptional accuracy and adaptability, understanding the decisionmaking processes within their complex neural networks remains challenging [START_REF] Ndukwe | A Graphical and Qualitative Review of Literature on AI-based Cyber-Threat Intelligence (CTI) in Banking Sector[END_REF]. This lack of transparency poses potential obstacles in pinpointing the root causes of false positives or understanding how the models arrived at specific threat classifications. Addressing this deficiency is crucial to enhance the trustworthiness of AI-based firewalls, especially in environments where interpretability and accountability are paramount [START_REF] Mishra | Exploring the Impact of AI-Based Cyber Security Financial Sector Management[END_REF].

Another identified deficiency pertains to the susceptibility of machine learning methods, particularly those relying heavily on historical data, to concept drift [START_REF] Ndukwe | A Graphical and Qualitative Review of Literature on AI-based Cyber-Threat Intelligence (CTI) in Banking Sector[END_REF]. As the cybersecurity landscape evolves, the nature of cyber threats undergoes dynamic changes, introducing new attack vectors and techniques [START_REF] Mishra | Exploring the Impact of AI-Based Cyber Security Financial Sector Management[END_REF]. Machine learning models, which are trained on historical data, may struggle to adapt promptly to these emerging threats, leading to delayed responses and potentially increased false negatives [START_REF] Mishra | Exploring the Impact of AI-Based Cyber Security Financial Sector Management[END_REF]. This deficiency highlights the need for continuous model updates, dynamic retraining, and the integration of real-time threat intelligence to ensure the resilience of AIbased firewalls against the ever-shifting nature of cyber threats.

Furthermore, the analysis exposed challenges related to the scalability of certain machine learning methods in large-scale network environments [START_REF] Jayalaxmi | Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey[END_REF]. As network infrastructures expand, some machine learning algorithms encounter limitations in efficiently processing the vast amounts of data, resulting in potential bottlenecks and compromised real-time threat detection [START_REF] Mishra | Exploring the Impact of AI-Based Cyber Security Financial Sector Management[END_REF]. Addressing scalability issues is pivotal for the practical implementation of AI-based firewalls in diverse and expansive network architectures, necessitating optimizations and innovations to ensure the seamless adaptation of these cybersecurity measures to the growing complexity of contemporary networks [START_REF] Jayalaxmi | Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey[END_REF].

Limitations of the Study

Scope of Evaluated Methods

The comparative analysis is based on a selection of AI-based firewall methods drawn from the literature review.

However, the rapidly evolving landscape of cybersecurity may introduce newer methodologies and advancements that were not covered in the study. Therefore, the findings are limited to the methods included in the analysis and may not encompass the latest developments in AI-based firewall technology.

Benchmarking Datasets

The effectiveness of AI-based firewall methods heavily relies on the quality and representativeness of the datasets used for training and evaluation. The study acknowledges the importance of labeled datasets for supervised learning but may not delve into the specific characteristics or potential biases present in the datasets used across different methods. Variability in dataset composition could impact the generalizability of the findings to diverse real-world scenarios.

Real-World Implementation Challenges

The analysis provides insights into the performance of AI-based firewall methods across various metrics, but realworld implementation involves additional challenges such as integration with existing network infrastructure, compliance with industry regulations, and considerations for user privacy. These practical aspects, critical for successful deployment, are beyond the scope of the performance metrics examined in the study.

Adversarial Tactics Realism

While the study explores the robustness of AI-based firewalls against adversarial attacks, the realism of the simulated adversarial tactics may differ from actual threats encountered in live network environments. Adversarial attacks are continually evolving, and the study's findings may not fully capture the diversity and sophistication of real-world adversarial strategies.

Interpretability Metrics

The study highlights the deficiency in interpretability and transparency of certain deep learning architectures.

However, it does not delve into specific metrics or methodologies for evaluating interpretability. A more detailed examination of interpretability metrics and techniques could provide a nuanced understanding of how well these AI-based firewall methods can be understood and trusted by cybersecurity practitioners.

Temporal Factors

The dynamic nature of the cybersecurity landscape implies that the performance of AI-based firewall methods may change over time. The study provides a snapshot of performance at a particular point, and the efficacy of these methods in the future could be influenced by emerging threats, advancements in technology, or changes in the regulatory environment.

Resource Constraints

The evaluation of computational efficiency does not consider potential resource constraints, especially in realworld scenarios where organizations may have limitations in terms of computing power, budget, or expertise.

Assessing the practical feasibility of implementing these methods under resource constraints is crucial for their widespread adoption.

Proposed Improvements

Addressing the identified deficiencies in AI-based firewalls necessitates strategic improvements and innovations across various dimensions. Firstly, enhancing the interpretability and transparency of deep learning architectures, such as CNNs and RNNs, is imperative. Introducing explainability mechanisms, such as attention mechanisms or layer-wise relevance propagation, can offer insights into the decision-making processes of these models [START_REF] Jayalaxmi | Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey[END_REF].

Additionally, developing visualization tools that provide a comprehensive understanding of the features influencing the model's output can empower cybersecurity professionals to interpret and trust the decisions made by deep learning-based AI firewalls [START_REF] Ndukwe | A Graphical and Qualitative Review of Literature on AI-based Cyber-Threat Intelligence (CTI) in Banking Sector[END_REF]. Furthermore, integrating interpretable deep learning architectures, where model decisions are more easily traceable, could bridge the gap between the high accuracy offered by these models and the need for transparency in cybersecurity decision-making [START_REF] Jayalaxmi | Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey[END_REF].

To mitigate the impact of concept drift, particularly in machine learning-based AI firewalls, a continuous learning framework should be embraced [START_REF] Ndukwe | A Graphical and Qualitative Review of Literature on AI-based Cyber-Threat Intelligence (CTI) in Banking Sector[END_REF]. This involves dynamic retraining of models with updated datasets that incorporate the latest threat intelligence and real-world attack scenarios. Implementing adaptive algorithms that autonomously adjust model parameters in response to evolving threats can enhance the resilience of AI-based firewalls [START_REF] Jayalaxmi | Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey[END_REF]. The integration of anomaly detection mechanisms that can quickly identify deviations from established norms, signaling potential concept drift, also contributes to early threat detection and response.

Additionally, fostering collaborations between cybersecurity professionals and data scientists to develop hybrid models that combine the strengths of both machine learning and deep learning approaches can create more robust and adaptive AI-based firewalls [START_REF] Jayalaxmi | Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey[END_REF].

Scalability challenges can be addressed through advancements in parallel processing and distributed computing.

Optimizing machine learning algorithms for parallel execution and leveraging the capabilities of distributed systems can significantly enhance the scalability of AI-based firewalls [START_REF] Jayalaxmi | Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey[END_REF]. Implementing cloud-based solutions and edge computing strategies can further distribute computational loads, ensuring that the performance of these cybersecurity measures remains efficient even in large-scale network environments. Collaboration with cloud service providers and leveraging advancements in edge computing technologies can facilitate the seamless integration of scalable AI-based firewalls into diverse network architectures [START_REF] Jayalaxmi | Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey[END_REF].

Furthermore, to fortify AI-based firewalls against adversarial attacks, incorporating advanced adversarial training techniques is vital [START_REF] Ndukwe | A Graphical and Qualitative Review of Literature on AI-based Cyber-Threat Intelligence (CTI) in Banking Sector[END_REF]. Adversarial training involves exposing the models to synthesized adversarial examples during training, enabling them to learn to recognize and defend against manipulative tactics [START_REF] Jayalaxmi | Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey[END_REF]. Regular updates to adversarial databases can ensure that AI-based firewalls are continuously trained on the latest adversarial strategies, enhancing their robustness. Implementing diverse defense mechanisms, including input diversification and feature-level defenses, can further bolster the resilience of these systems against adversarial manipulations.

The integration of human-centric AI models is another avenue for improvement, particularly in user-centric threat analysis. Recognizing the role of end-users in cybersecurity, the development of AI models that consider human behavior patterns and preferences can enhance the accuracy of threat detection [START_REF] Jayalaxmi | Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey[END_REF]. These models can leverage user-centric features such as browsing behavior, access patterns, and application usage to create personalized threat profiles. This approach not only increases the precision of threat identification but also minimizes the likelihood of false positives by aligning with user-specific norms and behaviors [START_REF] Ndukwe | A Graphical and Qualitative Review of Literature on AI-based Cyber-Threat Intelligence (CTI) in Banking Sector[END_REF].Lastly, to ensure regulatory compliance, AI-based firewalls should be designed with built-in mechanisms for transparent reporting and auditing. Implementing explainability features not only aids in understanding model decisions but also facilitates compliance with regulatory frameworks that mandate transparency in AI systems [START_REF] Jayalaxmi | Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey[END_REF]. Regular audits, both internal and external, can ensure that AI-based firewalls adhere to established compliance standards and data protection regulations [START_REF] Ndukwe | A Graphical and Qualitative Review of Literature on AI-based Cyber-Threat Intelligence (CTI) in Banking Sector[END_REF]. Collaboration with legal and regulatory experts can further guide the development of AI-based firewalls that not only provide robust cybersecurity measures but also align with legal and ethical considerations in various jurisdictions [START_REF] Ndukwe | A Graphical and Qualitative Review of Literature on AI-based Cyber-Threat Intelligence (CTI) in Banking Sector[END_REF].

Conclusion

In conclusion, the comparative analysis of AI-based firewalls has provided a holistic view of their strengths, weaknesses, and potential areas for improvement in the dynamic landscape of cybersecurity. The identified deficiencies, including interpretability challenges in deep learning architectures, susceptibility to concept drift, and scalability issues, underscore the need for strategic enhancements to propel these cybersecurity measures to new levels of effectiveness. The proposed improvements outlined above represent a roadmap for advancing the field, addressing these deficiencies, and fortifying AI-based firewalls against the ever-evolving nature of cyber threats.

The need for interpretability and transparency in deep learning models has been acknowledged as a crucial factor in fostering trust among cybersecurity professionals and end-users. The proposed improvements, encompassing explainability mechanisms and visualization tools, aim to demystify the decision-making processes within complex neural networks. By making these models more interpretable, the cybersecurity community can gain deeper insights into threat classifications and mitigate the risks associated with potential false positives or misinterpretations of AI-based firewall decisions.

Adapting to the dynamic nature of cyber threats requires a proactive approach to concept drift. The proposed continuous learning framework, dynamic retraining, and the integration of adaptive algorithms and anomaly detection mechanisms constitute a multifaceted strategy to enhance the adaptability of AI-based firewalls. This approach ensures that these cybersecurity measures stay ahead of emerging threats, offering a more resilient defense against novel attack vectors and evolving tactics employed by cyber adversaries. Adversarial training, coupled with regular updates to adversarial databases, ensures that these systems continuously evolve to recognize and defend against manipulative tactics employed by adversaries. This proactive stance is crucial in maintaining the integrity and reliability of AI-based firewalls in the face of sophisticated and evolving cyber threats.

Moreover, the integration of human-centric AI models, as proposed, recognizes the importance of considering end-users in cybersecurity. By leveraging user-centric features and behavior patterns, AI-based firewalls can provide a more personalized and accurate threat analysis. This approach aligns with the broader trend of humancentric cybersecurity, acknowledging the pivotal role of individual users in shaping the security posture of digital environments.

Lastly, ensuring regulatory compliance is integral to the success and adoption of AI-based firewalls. The proposed improvements include features that facilitate transparent reporting, auditing, and adherence to legal standards.

Collaboration with legal and regulatory experts, alongside regular internal and external audits, ensures that these cybersecurity measures not only excel in threat detection but also align with ethical, legal, and regulatory considerations.

In essence, the proposed improvements serve as a comprehensive strategy for advancing AI-based firewalls into the next generation of cybersecurity. The synergistic integration of interpretability, adaptability, scalability, resilience against adversarial attacks, human-centric features, and regulatory compliance measures can collectively elevate the effectiveness and trustworthiness of these cybersecurity solutions. As the cybersecurity landscape continues to evolve, these proposed improvements position AI-based firewalls as adaptive, robust, and ethical defenders against an ever-expanding array of cyber threats.

Figure 1 :

 1 Figure 1: Adaptability to new threats

Figure 2 :

 2 Figure 2: Scalability of AI-based Firewalls[START_REF] Ndukwe | A Graphical and Qualitative Review of Literature on AI-based Cyber-Threat Intelligence (CTI) in Banking Sector[END_REF]

Figure 3 :

 3 Figure 3: Detection Accuracy Graph[START_REF] Patil | Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning[END_REF]

  Scalability challenges, a critical concern in the context of large-scale network environments, are addressed through the proposed advancements in parallel processing, distributed computing, and cloud-based solutions. The goal is to create AI-based firewalls that seamlessly integrate into diverse network architectures, maintaining efficiency and real-time threat detection even as network infrastructures expand. Collaboration with cloud service providers and leveraging edge computing technologies play a pivotal role in optimizing the scalability of these cybersecurity measures.The fortification against adversarial attacks, as proposed through advanced adversarial training techniques and diverse defense mechanisms, reflects a commitment to enhancing the robustness of AI-based firewalls.

Table 1 :

 1 Performance Metrics Comparison Table[START_REF] Patil | Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning[END_REF]

	AI-Based	Detection	False	Computational	Adaptability to	Scalability	Robustness
	Firewall	Accuracy (%)	Positive	Efficiency	New Threats		Against
	Method		Rate (%)				Adversarial
							Attacks
	Method A	95	1.5	High	High	Scalable	Strong
	Method B	92	0.8	Moderate	Moderate	Limited	Moderate
	Method C	94	1.2	High	High	Scalable	Strong
	Method D	90	1.0	Low	Moderate	Limited	Moderate

International Journal of Computer (IJC) -Volume 49,[START_REF] Ai | Survey on the scheme evaluation, opportunities and challenges of software defined-information centric network[END_REF]


Sina Ahmadi
email: sina0@acm.org

A COMPREHENSIVE STUDY ON INTEGRATION OF BIG DATA AND AI IN FINANCIAL INDUSTRY AND ITS EFFECT ON PRESENT AND FUTURE OPPORTUNITIES

Keywords: big data, AI, financial industry, comparative study, openAI

This study evaluates the substantial influence of AI-technologies in the finance industry, with advancement expected to accelerate in the next few years. It also forecasts the expansion of AI adoption across various business sectors and the integration of AI-based operational networks with existing commercial systems to meet consumer demands. The financial industry will blend AI-based transaction channels with established systems, enhancing the customer experience and efficiency. This integration will streamline and advance transaction processes, making them more responsive to customer demands. The standard change in customer dealings is expected to be a significant transformation in the financial sector. The transformative potential of Big Data and AI in the financial sector goes beyond operational improvements; these technologies will create new opportunities for growth and development, giving financial institutions a modest point in operational efficiency and innovative product and service offerings. This study aims to investigate the overall impact of the convergence of Big Data and AI on the financial industry. It anticipates increased revolution, diversification of commercial applications, and smooth AI integration into existing systems. These technologies will shape the financial environment in the future, offering new opportunities for industry participants and consumers.

I. INTRODUCTION

Big Data analytics is typically the technical process of analyzing massive amounts of data to discover patterns, linkages, market trends, and customer preferences information that can assist businesses in making better-informed decisions. Artificial intelligence is the capability of computer systems to perform tasks that generally require human intelligence, such as speech recognition, decision-making, and pattern understanding. In the age of the digital revolution, the financial industry is undergoing significant changes. There has been a drastic shift in the way financial institutions operated in the past, and this has become possible by the integration of AI and Big Data in the finance industry.

Every single day, the financial industry generates enormous data, ranging from customer information to transaction records. All praises to digitalization, these data are not only kept confidential but can also be quickly provided to customers when required for strategic planning and decision-making. With the advancement of these digital technologies, enormous datasets can be efficiently handled, analyzed, and recorded. Furthermore, unstructured data can be converted to systematically structured Data. Scam detection, credit grading, and customer relationships can be managed more efficiently due to the integration of these technologies into the system [START_REF] Haldar | Big Data Analytics in Financial Services: A Review[END_REF]. In finance, deep reinforcement learning is used. Artificial intelligence, particularly reinforcement learning, can optimize hedging tactics in derivative markets [START_REF] Zohren | Deep Hedging: Hedging Derivatives Under Generic Market Frictions Using Reinforcement Learning[END_REF]. IoT and banking integration are evolving, with AI and Big Data becoming increasingly crucial in understanding data from interconnected financial equipment [START_REF] Tsai | Data mining for internet of things: A survey[END_REF]. While not entirely financial in nature, AI and machine learning have a broader impact on the future of labor. They provide insight into how these technologies, including their applications in finance, are influencing the labor market [START_REF] Chong | The Impact of Artificial Intelligence and Machine Learning on the Future of Work[END_REF].

Due to the integration of AI and Big Data in the finance industry, it is progressing toward digitalization and leading to an effective transformation. In addition to changing the financial services industry, these technologies are opening up previously unheard-of possibilities for expansion and innovation. Big Data offers a multitude of information that was previously incomprehensible. It is usually classified and identified based on its size, speed, and variations. AI tools, especially in machine learning (ML), aid in the evaluation and management of credit and its related risks in the finance industry [START_REF] Khandani | Consumer credit-risk models via machine-learning algorithms[END_REF]. Nowadays, financial markets are utilizing this information to gain awareness of market demands, customer preferences, and scam evaluations. The importance of investigating how the integration of Big Data and AI can influence the financial markets and provide insights into challenges and opportunities for the present and future cannot be underestimated. If the financial market aims to be innovative, and progressive, and provide customers with better services, it must acknowledge these changes. This research intends to evaluate the current influence of AI and Big Data in the finance industry, examining how they can contribute to the industry's future growth and identifying the necessary changes these industries should implement in their systems. In doing so, it aims to provide a complete image of the evolving financial landscape and assist stakeholders in exploiting the potential of these disruptive technologies.

The major objectives of this research are:

• To analyze the influence of AI and Big Data in various aspects of the financial industry.

• To analyze the current impact of AI and Big Data in the finance industry.

• To identify evolving demands and future opportunities created by AI and Big Data.

• To provide structured recommendations for financial institutes to adopt these technologies for growth and development.

II. LITERATURE REVIEW

The financial industry is undergoing an evolution due to the integration of Big Data and Artificial Intelligence (AI). This literature review examines the current state and prospects of this revolutionary impact on financial processes, decisionmaking, and services.

The 21st century has become a transformative era, due to the invention of AI, and has completely reshaped our society. AI has begun to transform the way we live, particularly in financial services, including banking transactions, stock investing, insurance, and loan services [START_REF] Yi Han | The Impact of Artificial Intelligence on the Financial Services Industry[END_REF]. It precisely covers a wide range of financial businesses, including crowdfunding platforms, operational peer-to-peer lending, wealth management, SME finance, and asset management platforms, trade management, crypto-currency, money/remittance transfer, mobile payments systems, and so on [START_REF] Hasan | Current landscape and influence of Big Data on finance[END_REF]. While technologies capable of performing human-based tasks cannot replace real humans, today's AI systems can perform activities that are well-defined and typically require human intelligence quite effectively [START_REF] El Bachir | Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance[END_REF].

A study highlighted the potential of blockchain technology in the financial sector, emphasizing its decentralized nature and the use of AI algorithms for secure and transparent transactions [START_REF] Bonneau | Research perspectives and challenges for Bitcoin and cryptocurrencies[END_REF]. Big Data plays a crucial role in the financial sector, integrating AI algorithms with vast datasets to provide actionable insights, enhance decision-making, and optimize operational efficiency [START_REF] Chen | Business Intelligence and Analytics: From Big Data to Big Impact[END_REF]. AI-driven analytics efficiently manage large volumes of financial data, enabling predictive modeling, risk assessment, and real-time decision-making for improved financial outcomes [START_REF] Chen | Big Data for Financial Analytics[END_REF]. Machine learning algorithms are being used to provide personalized financial recommendations, thereby improving customer experiences and increasing engagement and satisfaction [START_REF] García-Bariocanal | Personalization in Financial Institutions: Challenges and Opportunities[END_REF]. AI technologies are revolutionizing traditional financial practices, enhancing efficiency and accuracy [START_REF] Hwang | The impact of Big Data in finance and how machine learning is reshaping the industry[END_REF]. They are being used for fraud detection and portfolio optimization, automating complex financial tasks, and enhancing industry performance [START_REF] Kakouris | Machine learning in finance: A practical overview[END_REF]. A study emphasized the significant role of AI-powered Big Data analytics in detecting and preventing financial fraud, utilizing advanced algorithms to analyze vast datasets and detect anomalous patterns [START_REF] Lebuda | Financial Fraud Detection Using Big Data Analytics[END_REF]. Digitalization has boosted AI-based business models, with numerous institutions entering the market. Banks and insurers are either spinning off AI expertise or paying billions to acquire it. Machine learning has significantly enhanced market efficiency and investor returns by integrating it into trading strategies, risk management, and investment decisions [START_REF] Lo | Artificial intelligence and financial markets[END_REF]. Research on the ethical implications of AI and Big Data in finance has highlighted potential biases and the need for responsible AI practices in financial decision-making [START_REF] López-Cobo | Ethical and Societal Considerations of Big Data and Artificial Intelligence in Finance[END_REF]. Regulatory challenges might arise from the integration of Big Data and machine learning in finance, such as data privacy, algorithmic transparency, and the evolving regulatory landscape [START_REF] Nunziato | Big Data, Machine Learning, and the Regulatory State[END_REF].

III. IMPACT OF AI AND BIG DATA IN BANKING INDUSTRY AND PRESENT AND FUTURE OPPORTUNITIES

AI and Big Data are transforming the banking industry in various ways, including enhancing customer experience, improving risk management, increasing efficiency, and creating new business models. Fraud poses a significant challenge for banks and financial institutions, with potential losses reaching billions of dollars annually. These institutions often keep sensitive data online, increasing the risk of security breaches. As a result, technological advancements have made financial fraud a major threat. Earlier versions of fraud detection systems relied on a set of guidelines that today's fraudsters can easily circumvent. Therefore, an increasing number of businesses are turning to machine learning to identify and stop illegal financial activities. AI has the potential to help banks improve their efficiency, reduce costs, and enhance customer experience [START_REF] Li | Artificial Intelligence in Banking: New Frontiers[END_REF]. Here are some examples of how AI and Big Data are impacting the banking industry and the opportunities they present for both the present and the future:

• Customer Experience: The integration of Big Data analytics, artificial intelligence, and blockchain technologies in business and management opens doors for present and future opportunities. The potential of these technologies lies in transforming business operations and creating new business models through digitalization [START_REF] Li | Big Data Analytics, Artificial Intelligence, and Blockchain Technologies in Business and Management: A Systematic Review and Future Directions[END_REF]. AI and Big Data can help banks provide personalized and seamless services to customers through chatbots, voice assistants, robo-advisors, and recommendation systems. For example, ChatGPT is a language model that understands and generates human-like responses to user questions using natural language processing and machine learning. Banks can use ChatGPT and similar models to offer 24/7 customer service, financial advice, and product suggestions.

• Risk Management: AI and Big Data can help banks in detecting and preventing fraud, money laundering, cyberattacks, and other risks by analyzing large volumes of data and identifying patterns, anomalies, and behaviors. Cyber-security is an application of AI that is specifically designed to protect computer networks, programs, and data from attacks, illegal access, amendments, or destruction [START_REF] Buczak | A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection[END_REF] For example, AI can help banks monitor transactions, verify identities, flag suspicious activities, and alert customers and authorities in real time.

• Efficiency: Another significant contribution of AI is its automation, providing authentic insights and recognizing patterns and trends in datasets, thereby making tasks more efficient and less time-consuming [START_REF] Verma | Artificial Intelligence in Banking: An Analysis of Applications, Challenges, and Opportunities[END_REF]. This helps reduce costs, errors, and interruptions and enhances overall value and efficiency. For instance, Artificial intelligence assists banks in identifying the reliability of borrowers, providing their customers with financial loans, and hence processing their applications for loans in a smooth and transparent way.

• Business Models: Predictive analytics, social banking, behavioral finance, and open banking are some of the creative and novel services that banks offer, made possible by the tools of Big Data and AI in the Era of digitalization. These services improve the quality of banking, enhance customer relationships, and help create new revenue streams. For instance, artificial intelligence assists the banks in detecting data on the customer's preferences and offers them solutions to their problems within the time frame.

IV. IMPACT OF AI AND BIG DATA IN STOCK INDUSTRY AND PRESENT AND FUTURE OPPORTUNITIES

AI and Big Data are also significantly impacting the stock industry, enhancing trading strategies, improving market efficiency, and creating new investment opportunities. A survey has found that insurance claims are the most unsatisfactory part of the entire insurance product consumption process. This is because the claims process often requires a significant amount of detailed documentation. In case of dispute, multiple investigations and verifications are required, making the process complex and draining for customers. Moreover, at the same time, there are many cases of unreasonable claims in today's insurance industry. This can be attributed to policyholders hastily signing contracts without understanding the terms and conditions thoroughly. Additionally, some insurance companies also suffer from misleading sales and lax underwriting. Compared with traditional manual claims, intelligent claims can be quickly viewed, accounted for, and fixed, which can save a lot of time and improve the efficiency of claims processing. Here are some examples of how AI and Big Data are impacting the stock industry and its present and future opportunities:

• Trading Strategies: According to a paper published in The Journal of Finance, algorithmic trading has a positive influence on the financial market. The authors of the paper analyzed the effect of algorithmic trading on market quality and found that it narrows spreads, reduces adverse selection, and enhances the informativeness of quotes [START_REF] Hendershott | Does algorithmic trading improve liquidity?[END_REF]. AI and Big Data can help traders and investors analyze large amounts of data from various sources, including market prices, news, social media, and sentiment, to generate insights, signals, and predictions for optimal trading decisions. For instance, AI algorithms can perform trades more quickly, at lower costs, and with less human bias, while also having the potential to scan market anomalies and provide clear and transparent trading opportunities. Furthermore, AI plays a significant role in structuring trade patterns and adaptive trading strategies, enabling them to learn it deeply and give feedback to adapt according to the updated market.

• Market Efficiency: Efficiency and transparency in the stock market are maintained by AI and Big Data by enhanced liquidity, reduced information asymmetry, and lower transaction costs. For instance, AI becomes instrumental in aiding the market customers to get access and process their relevant information within their time frame including their earnings reports, financial statements, and corporate actions, and hence reduces hustle and bias. Besides this, detection of market mishandling, scams, and mismanagement are also eliminated, supervised, and regulated by AI.

• Investment Opportunities: In research conducted by [START_REF] Roncalli | Machine learning for factor investing[END_REF], the significance of machine learning (ML) in trading is explained. The study highlighted how AI tools enhance the validity of factor models and portfolio optimization [START_REF] Roncalli | Machine learning for factor investing[END_REF]. These tools help in the creation of innovative services such as robo-advisors, smart beta, and thematic investing. For instance, robo-advisors can provide personalized financial advice, automation, and portfolio management based on the investor's demands and preferences. Additionally, Smart Beta can integrate the benefits of both passive and active investing by the use of AI and Big Data. AI and Big Data are also used in Thematic investing to recognize and invest in long-term trends and themes, such as ESG, technology, and healthcare.

V. IMPACT OF AI AND BIG DATA IN INSURANCE INDUSTRY AND PRESENT AND FUTURE OPPORTUNITIES

AI and its tools have a significant influence on every aspect of insurance, from allocation to financing, pricing to claims. AI and its advanced technologies are already shaping distribution and underwriting, with strategies being valued, purchased, and bound in near real-time [START_REF] Balasubramanian | Insurance 2030-The impact of AI on the future of insurance[END_REF]. The stock market may be the most sophisticated technique. A person exchanges shares for stocks with an entrepreneur and gets some cash back in the meanwhile. Following this model can be a potential investment strategy. However, at this stage, costs and liquidity may not be stable. Machine learning is a kind of equipment to encourage us to achieve what we want. ML has different techniques and methods to implement the prediction framework, for instance, fundamental analysis, technique analysis, market computation, etc. Academic research suggests that the usage of artificial neural networks, comprising recurrent neural networks, which efficiently apply machine learning, is the most evident technique [START_REF] Sharma | Survey of stock market prediction using machine learning approach[END_REF]. Here are some of the main impacts and opportunities of AI and Big Data in the insurance industry:

• Customer Experience: AI and Big Data can help insurers provide personalized and seamless services to customers through chatbots, voice assistants, robo-advisors, and recommendation systems. These technologies can offer 24/7 customer service, financial advice, and product suggestions based on the customer's needs, preferences, and behaviors.

• Risk Management: AI and Big Data can help insurers detect and prevent fraud, money laundering, cyberattacks, and other risks by analyzing large amounts of data and identifying patterns, anomalies, and behaviors. These technologies can help insurers in monitoring transactions, verify identities, flag suspicious activities, and alert customers and authorities in real-time.

• Efficiency: Automation, which is an innovative and widely used tool of AI and Big Data, assists insurance market consumers in various processes such as underwriting, pricing, claims, compliance, reporting, and auditing. This helps reduce the cost of services, enhance value, and improve efficiency in the market. AI would also help to detect the reliability of the customers and provide them with insurance services in streamline. Not only that, it assists the insurers in getting loans in less time.

• Business Models: AI and Big Data help the insurance market to create services tailored to customers' preferences and demands, providing solutions to their financial problems within their timeframe. Due to these services provided by the industry there is rapid growth in consumer relationships loyalty, satisfaction, and revenue.

AI and Big Data are not only changing the financial industry, but also creating new challenges and opportunities for them. Some of the challenges include ensuring data quality, security, and privacy, complying with regulations and ethical standards, managing talent and skills, and fostering trust and transparency. Some of the opportunities include collaborating with FinTech and other players, leveraging cloud and edge computing, and adopting agile and customercentric approaches. To succeed in the AI and Big Data era, the financial industry needs to embrace innovation, transformation, and differentiation.

VI. METHODOLOGY

A. Study Type

This research adopted a quantitative cross-sectional approach through a survey to evaluate the influence of Big Data and AI in the Financial industry and current and future opportunities.

B. Scope of the Research

This research will focus on the following areas:

• Impact of Big Data in Finance: The different sources of Big Data and AI which are connected to the finance industry, data collecting methodology and insights can be obtained from it.

• AI in Finance: Various AI tools like ML Machine learning, Natural Language Processing (NLP), and Robotic Process Automation (RPA) and their relevance to the finance industry will be identified.

• Impact of AI and Big Data in Future Trends: This Study will explore the future demands of AI and Big Data in finance and how they can positively reshape their future.

VII. POPULATIONS AND SAMPLING

A. Target Population

The research was intended to evaluate the influence of AI and Big Data in the finance industry, so the target population is the financial industry. For this purpose, three main finance industries including banks, insurance, and stock industries are selected.

B. Sampling Method

Simple random sampling is used for this research.

C. Questionnaire Development

• Design: The questionnaire was designed according to the demand of research to evaluate the influence, challenges, and opportunities provided by AI and Big Data in the financial industry.

• Question Types: The questionnaire was divided into four portions. The first portion was for personal information, the second portion consisted of a set of questions that determine the level of awareness of Big Data and AI Data, the third portion was to assess if the industry is using these tools in their structure already and what current opportunities they are enjoying while using these tools, last section of the questionnaire contained open-ended questions. This section was constructed to know what future opportunities can be predicted for finance growth by utilizing these tools.

• Data Collection: Data for this research were collected through a questionnaire. The survey was conducted by physically distributing the questionnaire to various finance sectors, and data were collected in steps. The responses provided a practical view of the influence of AI and Big Data in the professional industry.

• Data Analysis: The results yielded from the survey underwent thematic analysis. Since open-ended questions were also used, themes and logic behind the answers would be identified.

This paper provides a comprehensive examination of AI and Big Data in various aspects of the finance business. The study also examines the current and future potential made available by the integration of Big Data and AI in the banking industry. Therefore, it is crucial to learn about finance industries such as banks, insurance industries, and the stock market to understand how Big Data and AI are altering the financial industry now and what opportunities may emerge in the future. The comparative aspects enrich the study by considering various perspectives, situations, or time frames within the financial industry. To support the facts and data, various forms of research have been incorporated into this study. After conducting various types of research, it is essential to properly review their findings and gather data from them. This study has integrated data from previous research work on AI into the analysis to improve the quality of this study. In addition, some questionnaires were physically distributed across different business sectors, and data was collected step by step. As a result, this research yields notable findings, which are detailed in the results section.

VIII. RESULTS

Significant results were generated from the survey conducted by the questionnaire. The main findings of the research indicated that the integration of AI and Big Data into the finance industry increased the current opportunities, such as increased business rates, customer satisfaction, less pricey, less time consuming, scam detection and management credibility, and creation of new opportunities in the industry.

A. Overview of AI and Big Data Integration

The integration of AI and Big Data analysis revealed comprehensive and efficient results in the financial industry. These results provide insights into adoption rates, key players, and emerging trends within the industry.

B. Experimental Research Findings

The results of the experiments used in this research provided valuable insights into the influence of AI and Big Data on financial growth. The experiments showed that when the financial industries integrated AI and Big Data into their systems, yielded positive outcomes. The following are some statistics of experiments conducted in different financial sectors. The experiments were carried out in different phases to precisely analyze the potential of AI and Big Data in these sectors. A detailed investigation of the implementation of AI and Big Data in different sections of finance was conducted. The results showed that these tools have significant potential and influence in the finance industry, with widespread applications in credit scoring, fraud detection, and customer relationships.

D. Current and Future Potential in Banking

This study emphasized the current and future influence of AI and Big Data in the finance industry. It was found that when AI and Big Data were integrated into the financial industry's structure, areas such as credit management, operations, trading, and consumer relationships reached to a more advanced level. Moreover, it shifted the entire orthodox system towards digitalization and automation, which was less time-consuming and had the least bias and scam.

E. Understanding the Changing Aspects of Finance Industry

To understand and identify the evolving structure of the finance industry, data were collected from the most economically contributing industries of the system i.e. Banks, stock, and insurance market. The study involved a comparative analysis to evaluate precise results, considering different scenarios, situations, and time frames within the finance market.

F. Integration of AI and Big Data into the Study

As the primary method of data collection was used in this research, previous data and AI analysis were also incorporated into the study to conduct a comparative analysis of Big Data and AI and their influence on the financial industry. This approach also predicted the current influence of these tools in the market and what type of opportunities they can provide in the future.

Table 2: Questionnaire-based Data Collection

A comparative analysis between AI and Big Data was done to check the efficiency and demand of both and the following results were achieved.

IX. CHALLENGES AND CONSIDERATIONS

The research acknowledges the ethical implications of AI and Big Data in finance, emphasizing the importance of responsible practices. Regulatory challenges, including data privacy and algorithmic transparency, are recognized as potential obstacles to the widespread adoption of AI and Big Data in the financial industry.

X. FUTURE TRENDS AND OPPORTUNITIES

The study predicts that if AI and Big Data are used properly, they can significantly transform existing financial trends and quality to a diversified and progressed level. It also predicts that in the current scenario AI and Big Data can be used in a more efficient way to shape the future to provide the customer with more valuable opportunities.

XI. RECOMMENDATIONS

• For a successful and diversified financial system, AI and Big Data must be used for generating quality data, validation, data cleansing, and governance.

• Cybersecurity is another significant application of AI and Big Data that should be incorporated into financial setups to ensure the confidentiality of sensitive data, detect scams, and ensure transparent audits.

• Financial sectors should also implement talent management strategies and provide training in ML and Data Science to acquire individuals with expertise in digitalization and deep learning.

XII. CONCLUSION

In conclusion, this investigation has explained that if a structured implementation of AI and Big Data is done in the finance sector it can raise the potential of financial industries to the next level. This represents not only a transformation of the current functionalities within financial industries but also a significant step toward progression and development. The combination of AI and Big Data is highly innovative, if used properly, it has the potential to reshape current opportunities and nurture future opportunities for the welfare of mankind.

Figure 1 :

 1 Figure 1: Impact of AI and Big Data in Financial Industry

Table 1 :

 1 Experimental Research Results

C. Thorough Examination of AI and Big Data in Finance

	AI Implementation	Big Data Integration	Outcome
	Pilot phase	-	Initial positive feedback from customer service.
	Full deployment	Pilot phase	10% reduction in manual processing time.
	Advanced features	Full integration	15% increase in fraud detection accuracy.
	AI-driven insights	Expanded usage	20% improvement in personalized services.
	Enterprise-wide	Predictive analytics	30% enhancement in decision-making processes.