HD-I-IoT: Hyperdimensional Computing for Resilient Industrial Internet of Things Analytics

Industrial Internet of Things (I-IoT) enables fully automated production systems by continuously monitoring de-vices and analyzing collected data. Machine learning (ML) methods are commonly utilized for data analytics in such systems. Cyberattacks are a grave threat to I-IoT as they can manipu-late legitimate inputs, corrupting ML predictions and causing disruptions in the production systems. Hyperdimensional (HD) computing is a brain-inspired ML method that has been shown to be sufficiently accurate while being extremely robust, fast, and energy-efficient. In this work, we use non-linear encoding-based HD for intelligent fault diagnosis against different adversarial attacks. Our black-box adversarial attacks first train a substitute model and create perturbed test instances using this trained model. These examples are then transferred to the target models. The change in the classification accuracy is measured as the difference before and after the attacks. This change measures the resiliency of a learning method. Our experiments show that HD leads to a more resilient and lightweight learning solution than the state-of-the-art deep learning methods. HD has up to 67.5% higher resiliency compared to the state-of-the-art methods while being up to
$25.1\times$
faster to train.