A Mathematical Framework for Understanding and Countering Packet-Mutation Adversarial Attacks on NIDS

This paper presents a comprehensive mathematical framework for understanding and countering packet-mutation adversarial attacks on Network Intrusion Detection Systems (NIDS). We introduce the Adaptive Sequence-Aware Packet Mutation (ASAPM) algorithm for generating sophisticated adversarial examples and propose a novel defense mechanism, the Adaptive Ensemble with Perturbation Analysis (AEPA). Our approach leverages a diverse ensemble of machine learning models, including RNN-LSTM, GBDT, One-class SVM, and Transformer-based models, combined with dynamic weight adjustment and confidence calibration techniques. Experiments conducted on the UNSW-NB15 and CIC-IDS2017 datasets demonstrate the effectiveness of our methods. The ASAPM attack achieved high evasion rates of 60% and 50% respectively on these datasets with minimal perturbations. Conversely, our AEPA defense mechanism significantly outperformed baseline methods, improving the detection rate by 19.3% (from 72.4% to 91.7%) and reducing the false positive rate by 5.2% (from 8.3% to 3.1%). The adaptive nature of AEPA showed continuous improvement over time, with the detection rate increasing by 7.5% over 1000 iterations. This research contributes to the development of more resilient and adaptive NIDS, addressing the critical challenge of adversarial attacks in an evolving cybersecurity landscape.