Undermining Trust: How Bit-Flip Attacks Compromise Anomaly-based Network Intrusion Detection Systems

The rapid expansion of IoT leads to numerous security vulnerabilities, making IoT ecosystems attractive targets for cyberattacks. Anomaly-based Network Intrusion Detection Systems (aNIDS), powered by deep learning models, are widely used to enhance security in IoT environments; however, these models are inherently sensitive to physical-level attacks like bitflips, which can manipulate the model’s weights and degrade performance. While extensive research has explored deep learning security, there is a notable gap in the study of hardware-based bit-flip attacks on aNIDS in IoT. In this paper, we propose a benchmarking framework with four attack scenarios to evaluate the resilience of aNIDS against bit-flip attacks. The experimental results, using three IoT datasets, demonstrate the significant degradation of model performance under different attack schemas. The findings highlight the need for robust countermeasures to secure aNIDS models against bit-flip attacks in IoT networks.