Data Guardian: A Data Protection Scheme for Industrial Monitoring Systems

Recently, data-driven industrial monitoring systems have been rapidly developed and significantly improved the performance on industrial monitoring tasks. However, the widely deployed data-driven models expose industrial data to more unsecured links, which significantly increase the safety risk of safe-critical industrial systems. The research about adversarial attacks has shown that the potential attackers can utilize tiny crafted perturbations on the input data to mislead the machine learning models’ output. In this article, a novel cross-domain data protection scheme named “Data Guardian” is proposed to authenticate and correct industrial data under potential attacks on data-driven monitoring systems. “Data Guardian” embeds designed redundancy information into the data least significant bits, based on $q$-ary low-density parity-check (LDPC) codes over the Galois field (finite field). The data are encoded at the secured industrial sites and then decoded before being input into the monitoring systems, in which the security risk is usually higher. The decoding capability of $q$-ary LDPC codes is improved by the data statistical characteristics, with a new proposed prior estimation method. In the experiments, “Data Guardian” is tested under the attacks to the fault diagnosis models on the Tennessee Eastman process and rolling element bearing from Case Western Reserve University. The results show that “Data Guardian” can efficiently reduce the success rate of adversarial attacks, especially when the attack variable ratio is small.