  Insider threat is one of the most pressing threats in the field of
information security as it leads to huge financial losses by the companies.
Most of the proposed methods for detecting this threat require expensive and
invasive equipment, which makes them difficult to use in practice. In this
paper, we present a non-invasive method for detecting insider threat based on
stress recognition using keystroke dynamics assuming that intruder experiences
stress during making illegal actions, which affects the behavioral
characteristics. Proposed method uses both supervised and unsupervised machine
learning algorithms. As the results show, stress can provide highly valuable
information for insider threat detection.
