Hardening the IoT Edge: A TRADES-Based Approach for Robust Network Intrusion Detection
TL;DR
This work seeks to prove that TRADES, which uses KL divergence regularization, does not exhibit this trade-off on IoT tabular data, and shows that TRADES enables aligning the decision boundaries of the DNN with causal traffic feature spaces rather than vulnerable anomalies.
Abstract
Deep-learning-based Network Intrusion Detection Systems (NIDS) play a vital role in protecting Internet of Things (IoT) environments; however, they remain vulnerable to adversarial examples, in which small input perturbations can cause misclassification. Previous research has considered that there exists a trade-off between model accuracy and robustness; however, this work seeks to prove that TRADES, which uses KL divergence regularization, does not exhibit this trade-off on IoT tabular data. It has been shown that TRADES enables aligning the decision boundaries of the DNN with causal traffic feature spaces rather than vulnerable anomalies. For the Bot-IoT dataset, TRADES has achieved almost full robustness (~100%) against three different attacks including PGD, FGSM, and C&W in a bounded threat model ( $\epsilon $ =0.1). In addition, the TRADES framework has improved the robustness of models in case of data brittleness through using SMOTE method on NSL-KDD imbalanced dataset, leading to an increase of 55 points in robustness. Furthermore, cross-dataset evaluation on UNSW-NB15 and CICIDS2017 datasets has demonstrated good generalization properties (86.10% and 93.20% PGD robustness). Apart from theoretical findings, we provide practical validation for edge deployment within sub-millisecond latency (0.03 ms/packet) based on a quantized 150 KB TensorFlow Lite model, demonstrating promising potential for deployment in IoT devices using latency measurements.