Abstract
The advancement of Industrial Internet-of-Things (IIoT) magnifies the cyber risk of automated production lines, especially to deception attacks that tamper with the monitoring data to prevent the manipulated operation of production lines from being detected. To address this issue, we propose Stochastic Manufacturing (StoM), a new paradigm of manufacturing that is resistant to deception by design. StoM voids the foundation of deception attacks — i.e., the highly predictable operation data due to the cyclical manufacturing process — by injecting controlled stochasticity into the operation of production lines without degrading manufacturing efficiency or quality. StoM then examines if this stochasticity can be observed from the operation data and triggers an alarm of deception attack if not. We have experimentally evaluated StoM on two production line platforms, showing StoM to detect deception attacks with a detection rate exceeding 99.1%, a false alarm rate below 0.1%, and a latency of less than 1.2 manufacturing cycles. Our empirical analysis also shows that it is highly impractical for attackers to spoof the controlled stochasticity.