Perturbation-Resilient for Temporal-Camouflaged IoT Attacks
Xiaohui Li, Yuanyuan Li, Zhentian Zhong, Linfeng Tan, Junfeng Wang, Jiayong Liu · IEEE Internet of Things Journal · 2025
The growing adoption of Internet of Things (IoT) devices has introduced significant challenges to network security due to their heterogeneous nature and temporal pattern vulnerabilities. Among the emerging threats, adversarial attacks targeting IoT network traffic have gained attention for their ability to evade traditional and machine learning-based Network Intrusion Detection Systems (NIDS). While prior work has focused on static adversarial perturbations, these approaches fail to account for the temporal dynamics inherent in IoT traffic. IoT networks exhibit time-dependent patterns driven by device behavior, environmental factors and user interactions, creating an opportunity for more sophisticated adversarial strategies. This paper introduces a co-evolutionary adversarial framework termed dynamic Adversarial Temporal-Camouflaged Perturbation (ATCP) for IoT network attack traffic. ATCP dynamically segments network traffic into temporal intervals and applies targeted adversarial perturbations to each segment. By leveraging the temporal characteristics of IoT traffic, the proposed method generates subtle yet effective adversarial modifications that confuse NIDS by disrupting their ability to model time-dependent traffic patterns. Unlike static perturbation methods, ATCP provides valuable insights into adversarial attack methodologies, lays the foundation for developing more robust IoT security frameworks, and adapts to evolving traffic dynamics, making it a more effective and robust NIDS in real-world scenarios. Extensive experiments conducted on real-world IoT network datasets demonstrate that the proposed method achieves high evasion rates against Machine Learning (ML) based NIDS while preserving the functional integrity of IoT communications. Notably, among the four NIDS evaluated, KitNET experiences the most significant degradation, with its detection rate dropping from 93.07% to 18.55% after applying ATCP. Furthermore, ATCP exhibits strong adaptability across diverse IoT device types and network configurations, highlighting its generalizability.