Novel EEG Sensor-Based Risk Framework for the Detection of Insider Threats in Safety Critical Industrial Infrastructure

The loss or compromise of any safety critical industrial infrastructure can seriously impact the confidentiality, integrity, or delivery of essential services. Research has shown that such threats often come from malicious insiders. To identify these insiders, survey- and electrocardiogram-based approaches have been proposed; however, these approaches cannot effectively detect or predict any malicious insiders. Recently, electroencephalograms (EEGs) have been suggested as a potential alternative to detect these potential threats. Threat detection using EEG would be highly reliable as it overcomes the limitations of the previous methods. This study proposes a proof of concept for a system wherein a model trained using a deep learning algorithm is employed to evaluate EEG signals to detect insider threats. The algorithm can classify different mental states based on four category risk matrices. In particular, it analyses brainwave signals using long short-term memory (LSTM) designed to remember the previous mental states of each insider and compare them with the current brain state for associated risk-level classification. To evaluate the performance of the proposed system, we performed a comparative analysis using logistic regression (LR)—a predictive analysis technique used to describe the relationship between one dependent binary variable and one or more independent variables—on the same dataset. The experimental results obtained suggest that LSTM can achieve a classification accuracy of more than 80% compared to LR, which yields a classification accuracy of approximately 51%.