Machine Learning Algorithms for Anomaly Detection in Next-Gen AI-Based Firewalls: A Performance Analysis

With the increasing sophistication of cyber threats, the integration of artificial intelligence (AI) into next-generation firewalls has become imperative for robust cybersecurity. This research focuses on the application of machine learning algorithms for anomaly detection within AI-based firewalls, aiming to enhance the overall security posture of modern networks. The study explores various machine learning techniques, including but not limited to neural networks, support vector machines, and decision trees, evaluating their effectiveness in identifying anomalous patterns indicative of potential cyber threats. The research methodology involves the development of a comprehensive dataset representing normal network behavior and various cyber threats. Subsequently, a comparative analysis is conducted to assess the performance of different machine learning algorithms in terms of accuracy, precision, recall, and false positive rates. The experimental setup considers real-world scenarios and diverse attack vectors to ensure the generalizability of the findings.