Insider Threat

It has long been recognized that solutions to insider threat are mainly user-centric and several psychological and psychosocial models have been proposed. However, user behavior underlying these malicious acts is still not fully understood, motivating further investigation at the neuro-physiological level. In this work, we conduct a multi-modal study of how users-brain processes malicious and benign activities. In particular, we focus on using Electroencephalogram (EEG) signals that arise from the user's brain activities and eye tracking which can capture spontaneous responses that are unfiltered by the conscious mind. We conduct human study experiments to capture the Electroencephalogram (EEG) signals for a group of 25 participants while performing several computer-based activities in different scenarios. We analyze the EEG signals and the eye tracking data and extract features and evaluate our approach using several classifiers. The results show that our approach achieved an a...

THE IMPORTANCE OF PROTECTING NUCLEAR POWER plants, laboratories, and other facilities can hardly be overstated, especially in light of increased threats of terrorism. But the two principal components of nu-clear facility security—the appropriate security equipment and written ...

Insider threat is a major issue in cyber and corporate security. In this paper we study the psychosocial perspective of the insider via social media, Open Source Intelligence, and user generated content classification. Inductively, we propose a prediction method by evaluating the predisposition towards law enforcement and authorities , a personal psychosocial trait closely connected to the manifestation of malevolent insiders. We propose a methodology to detect users holding a negative attitude towards authorities. For doing so we facilitate the use of machine learning techniques and of a dictionary-based approach, so as to detect comments expressing negative attitude. Thus, we can draw conclusions over a user behavior and beliefs via the content the user generated within the limits a social medium. We also use an assumption free flat data representation technique in order to decide over the user's attitude. Furthermore, we compare the results of each method and highlight the common behavior manifested by the users. The demonstration is applied on a crawled community of users on YouTube.