Adversarial-HD: Hyperdimensional Computing Adversarial Attack Design for Secure Industrial Internet of Things

Abstract

Industrial Internet of Things (IIoT) is a collaboration of sensors, networking equipment, and devices to collect data from industrial operations. IIoT systems possess numerous security vulnerabilities due to inter-connectivity and limited computational power. Machine learning based intrusion detection system (IDS) is one possible security approach that continuously monitors network data and detects cyberattacks in an automated manner. Hyper-dimensional (HD) computing is a brain-inspired ML method that is sufficiently accurate while being extremely robust, fast, and energy-efficient. Based on these characteristics, HD can be a favorable ML-based IDS solution for IIoT systems. However, its prediction performance is impacted by small perturbations in the input data. To fully evaluate the vulnerabilities of HD, we propose an effective HD-oriented adversarial attack design. We first select the most diverse set of attacks to minimize overhead, and eliminate adversarial redundancy. Then, we perform a real-time attack selection which finds out the most effective attack. Our experiments on a realistic IIoT intrusion data set show the effectiveness of our attack design. Compared to the most effective single attack, our design strategy can improve attack success rate by up to 36%, and F1 score by up to 61%.