Towards Insider Threat Detection Using Psychophysiological Signals
Yassir Hashem, Hassan Takabi, Mohammad GhasemiGol, Ram Dantu · 2015
Insider threat is one of the greatest concerns for the information security system that could cause greater financial losses and damages than any other attacks. Recently many studies have been proposed to monitor and detect the insider attacks. However, implementing an effective detection system is a very challenging task. In this paper, we investigate the usability of human bio-signals to detect the malicious insiders in real time. We present an insider threat monitoring and detection framework based on the electroencephalography (EEG) signals to distinguish between normal and malicious activities. We describe the framework and its components. We then evaluate the proposed framework using several real world scenarios. The results show that the detection accuracy of the malicious activities is up to 90% and demonstrate that electroencephalography (EEG) can reveal valuable knowledge about the user behaviors and could be a very effective solution for detecting insider threats.