Data Guardian: A Data Protection Scheme for Industrial Monitoring Systems

Abstract

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, increasing 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 model outputs. In this paper, 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 (LSB), based on q-ary low density parity-codes (LDPC) over Galois field (finite field). The data is encoded at the secured industrial sites, then decoded before being input into the data-driven monitoring systems, where 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, the Data Guardian is tested under the attacks to the fault diagnosis models on Tennessee Eastman process (TEP) and rolling element bearing (REB) 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.