Enhancing Network Intrusion Detection: A Real-time Adaptive Framework for Temporal Evasion Attack Generation and Mitigation

Network Intrusion Detection Systems face increasing challenges from sophisticated evasion techniques that manipulate traffic timing patterns. This paper presents a Temporal Evasion Generation Algorithm (TEGA) for creating adversarial examples by exploiting temporal vulnerabilities, and an Adaptive Temporal Defense System (ATDS) to counter these attacks. We formalize temporal evasion mathematically and evaluate both systems using the CIC-IDS2018 dataset. TEGA achieves evasion success rates of 72.4%, significantly outperforming conventional techniques such as standard delay injection (45.6%) and burst pattern manipulation (53.2%). Conversely, ATDS demonstrates robust defense capabilities, with detection accuracy reaching 95% after adaptation and false positive rates reduced to 1.5%. Our comparative analysis reveals that sequence-based feature extraction combined with SVM classification provides optimal resilience against temporal evasion. The adaptive framework rapidly responds to new attack patterns, typically requiring only 2-3 update cycles to achieve over 90% detection accuracy. This research contributes to network security by addressing an emerging attack vector while offering promising directions for developing next-generation intrusion detection systems.