Novel EEG risk framework to identify insider threats in national critical infrastructure using deep learning techniques

The Cybersecurity of organization is becoming quite alarming especially in National Critical Infrastructure (NCI) as to protect their sensitive information and other valuable assets. A lot of focus has been done in managing outside attacks of data in organizations. Including Cyber-Physical System (CPS), which is a complex mixture of physical and computer components typically monitored or controlled by computer-based algorithms. However, there has been need to safeguard insider's behavior of breaching the expected code of conduct in maintaining the critical organizations' data and assets. The technology is highly reliable as it cannot be easily fabricated. The analysis of the brainwave signal will be performed using an advanced deep learning algorithm called Long Short Term Memory Recurrent Neural Network (LSTM-RNN) classifier which will remember a previous mental states of each insider and compare it with new present brain state to classify the risk level associated. The brain wave is also analysed with Adaptive Machine learning Algorithm which combines several weak learners which is decision trees, to form a single strong learner. In this study, our targets is to increase the security of NCI by providing a significant proof of concept system to detect insider threats through fitness evaluation using EEG signals that gets analyzed using deep learning algorithm which will classify different mental states into four categories risk matrix.