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    Novel EEG sensor-based risk framework for the detection of insider threats in safety critical industrial infrastructure

    Al Hammadi, A. and Lee, D. and Yeun, C.Y. and Damiani, E. and Kim, S.-k. and Yoo, Paul D. and Choi, H.-j. (2020) Novel EEG sensor-based risk framework for the detection of insider threats in safety critical industrial infrastructure. IEEE Access 8 , pp. 206222-206234. ISSN 2169-3536.

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    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 this end, survey- and electrocardiogram-based approaches were suggested to identify these insiders; 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; this 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 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 perform a comparative analysis using logistic regression (LR)—a predictive analysis used to describe the relationship between one dependent binary variable and one or more independent variables—on the same dataset. The experiment results suggest that LSTM can achieve a classification accuracy of more than 80% compared to LR, which yields a classification accuracy of approximately 51%.


    Item Type: Article
    Additional Information: (c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    Keyword(s) / Subject(s): Deep learning, EEG sensors, fitness evaluation, insider threats, LSTM, safety critical industrial infrastructure
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Data Analytics, Birkbeck Institute for
    Depositing User: Paul Yoo
    Date Deposited: 23 Nov 2020 11:40
    Last Modified: 09 Aug 2023 12:49


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