Next Generation AI-Based Firewalls: A Comparative Study

Abstract

Cybersecurity is a critical concern in the digital age, demanding innovative approaches to safeguard sensitiveinformation and systems. This paper conducts a thorough examination of next-generation firewalls (NGFWs) thatintegrate artificial intelligence (AI) technologies, presenting a comparative analysis of their efficacy. Astraditional firewalls fall short in addressing modern cyber threats, the incorporation of AI provides a promisingavenue for enhanced threat detection and mitigation. The literature review explores existing research on AI-basedfirewalls, delving into methodologies and technologies proposed by leading experts in the field. A compilation of20-25 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 performancemetrics, laying the groundwork for a comprehensive analysis. The methodology section outlines the systematicapproach employed to compare different AI-based firewall methods. Leveraging machine learning and deeplearning 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 weaknessesinherent in each approach, facilitating an informed evaluation. The comparative analysis section employsgraphical representations to elucidate the findings, offering a visual overview of the performance disparitiesamong selected AI-based firewall methods. Pros and cons are meticulously examined, providing stakeholderswith valuable insights for decision-making in cybersecurity strategy. This research aims to contribute to theongoing discourse on AI-based firewalls, addressing current limitations and paving the way for advancements thatfortify the cybersecurity landscape.