RES-HD: Resilient Intelligent Fault Diagnosis Against Adversarial Attacks Using Hyper-Dimensional Computing
This addresses the threat of cyber-attacks disrupting automated production systems, offering a more resilient and efficient solution, though it appears incremental as it applies an existing method (HDC) to a specific domain.
The paper tackles the problem of adversarial attacks on machine learning models for fault diagnosis in Industrial Internet of Things, showing that hyper-dimensional computing achieves up to 67.5% higher resiliency and 25.1% faster training compared to state-of-the-art deep learning methods.
Industrial Internet of Things (I-IoT) enables fully automated production systems by continuously monitoring devices and analyzing collected data. Machine learning methods are commonly utilized for data analytics in such systems. Cyber-attacks are a grave threat to I-IoT as they can manipulate legitimate inputs, corrupting ML predictions and causing disruptions in the production systems. Hyper-dimensional computing (HDC) is a brain-inspired machine learning method that has been shown to be sufficiently accurate while being extremely robust, fast, and energy-efficient. In this work, we use HDC 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 HDC leads to a more resilient and lightweight learning solution than the state-of-the-art deep learning methods. HDC has up to 67.5% higher resiliency compared to the state-of-the-art methods while being up to 25.1% faster to train.