Enhancing Network Intrusion Detection Systems: A Real-time Adaptive Machine Learning Approach for Adversarial Packet-Mutation Mitigation

Network Intrusion Detection Systems (NIDS) are increasingly vulnerable to sophisticated packet-mutation attacks that evade traditional detection methods. This paper presents a runtime adaptive machine-learning strategy to combat such adversarial attacks. We introduce an Adaptive Layered Mutation Algorithm (ALMA) for generating advanced adversarial examples and a runtime adaptive learning framework for real-time detection and response. Our approach integrates these components to create a robust, self-evolving NIDS. Experiments comparing various feature extractors and machine learning classifiers demonstrate that our adaptive approach achieves up to $\mathbf{9 8 \%}$ detection accuracy, significantly improving the identification of mutated packets over static models. The integrated system rapidly adapts to new attack patterns, achieving over $\mathbf{9 0 \%}$ detection accuracy for novel attacks within 2-3 update cycles. This research contributes to network security by presenting an adaptive, high-performance approach to intrusion detection that effectively addresses challenges posed by evolving packet-mutation attacks, offering promising directions for next-generation NIDS development.