Insider Threat Detection Based on Stress Recognition Using Keystroke Dynamics
This addresses insider threats in information security, which cause financial losses, but the approach is incremental as it builds on existing keystroke dynamics methods.
The paper tackles insider threat detection by proposing a non-invasive method based on stress recognition using keystroke dynamics, assuming intruders experience stress during illegal actions, and shows that stress provides highly valuable information for this purpose.
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.