ROLDEF: RObust Layered DEFense for Intrusion Detection Against Adversarial Attacks

The Industrial Internet of Things (IIoT) includes networking equipment and smart devices to collect and analyze data from industrial operations. However, IloT security is challenging due to its increased inter-connectivity and large attack surface. Machine learning (ML)-based intrusion detection system (IDS) is an IloT security measure that aims to detect and respond to malicious traffic by using ML models. However, these methods are susceptible to adversarial attacks. In this paper, we propose a RObust Layered DEFense (ROLDEF) against adversarial attacks. Our denoising autoencoder (DAE) based defense approach first detects if a sample comes from an adversarial attack. If an attack is detected, adversarial component is eliminated using the most effective DAE and the purified data is provided to the ML model. We use a realistic IloT intrusion data set to validate the effectiveness of our defense across various ML models, where we improve the average prediction performance by 114% with respect to no defense. Our defense also provides 50 % average prediction performance improvement compared to the state-of-the-art defense under various adversarial attacks. Our defense can also be deployed for any underlying ML model and provides an effective protection against adversarial attacks.