  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.
