Hybrid AI Frameworks for Real-Time Intrusion Detection and Threat Mitigation

Abstract

Conventional, static security systems are also out of date as they do not match the complexity and number of cyberattacks on our physical environment. On the other hand, the use of AI techniques in cybersecurity has also been emerged to detect and counteract dynamic threats]. This article studies design and performance of a hybrid AI model that combines ML, DL, and knowledge based reasoning for real-time IDS. Such hybridized architectures comprise of supervised classification models coupled with unsupervised anomaly detection or reinforcement learning-based decision-making modules that exploit their structural encodings to dynamically differentiate between known and unknown threat faster and better, yet significantly reducing the occurrence of false positives.Theoretically, the paper argues that for SF-Hybrid AI, it does not only cover a crucial place between pattern recognition and context understanding in computer science; but has indeed laid the foundation of constructing cyber defence ecosystems which are autonomous and self-healing.