Published Paper Details:

Traditional firewalls based on predefined rules and signatures to detect ,find and block malicious data, always struggling to adapt to evolving cyber threats. To remove this limitation, Artificial Intelligence (AI)-based firewalls leverage machine learning (ML) and deep learning (DL) techniques to dynamically analyze network behavior, find out anomalies, and prevent cyberattacks in real time. By using supervised, unsupervised, and reinforcement learning models, AI-powered firewalls can identify zero-day exploits, advanced persistent threats (APTs), malacious and suspicious patterns with higher accuracy than conventional firewall. Key AI techniques used include: Some key AI technic available here: o Threat Detection,Behavioral analysis, Automated Rule optimization, predictive threat Intelligence. o Threat Detection: Identifying deviations from normal traffic behavior using clustering and neural networks. o Behavioral Analysis: Monitoring user and device activity to detect inside threats. o Predictive Threat Intelligence: Using historical and past data to predict potential attacks. o Automated Rule Optimization: Automatic and Continuously updating firewall policies based on real-time threat analysis. AI-based firewalls enhance security by reducing false positives, enhance threat response times, and adapting to new attack vectors autonomously. However, challenges such as adversarial attacks, model interpretability, and calculated overhead remain areas of ongoing research. Future advancements in explainable AI (XAI) and federated learning may further strengthen AI-driven firewall solutions, making them indispensable in next-generation cybersecurity frameworks.