Explainable Artificial Intelligence to Evaluate Industrial Internal Security Using EEG Signals in IoT Framework

Abstract

Industrial insider threat detection has consistently been a popular field of research. To help detect potential insider threats, the emotional states of humans are identified through a wide range of physiological signals including the galvanic skin response, electrocardiogram, and Electroencephalogram (EEG). This research presents an insider risk assessment security evaluation usingEEGbrainwave signals with explainable Deep learning (DL) and Machine learning (ML) algorithms to classify abnormal EEG signals indicating a potential insider threat and evaluating Fitness for duty (FFD). The system is designed to be cost-effective by using an Emotiv Insight EEG device with five electrodes. In this study, the data from 24 people in different emotional states were collected. The different levels of emotions were mapped and classified into four risk levels, namely low, normal, medium, and high. The data were collected while the subjects were presented with different images from the scientific international affective picture system. The collected EEG signals were preprocessed to eliminate noise from physical movements and blinking. The data were then used to train self-feature learning of Two- and One-dimensional convolutional neural networks (1DCNN), Long short-term memory (LSTM), Adaptive boosting (AdaBoost), Random forest (RF), and k-nearest neighbors (KNN) models; the proposed method yielded classification accuracies of 96, 75, 80, 97, 94 and 81%, respectively.

Date of Award	Dec 2021
Original language	American English