Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms

• Published in: Applied sciences Vol. 9; no. 19; p. 4018
• Main Authors: Kim, Junhong, Park, Minsik, Kim, Haedong, Cho, Suhyoun, Kang, Pilsung
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2019
• Subjects: Algorithms; anomaly detection; behavioral model; Classification; Datasets; e-mail network; Electronic mail systems; Employees; insider threat detection; Knowledge; latent dirichlet allocation; Machine learning; Subject specialists; Threats; Universal Serial Bus; User behavior; United States > US

Insider threats are malicious activities by authorized users, such as theft of intellectual property or security information, fraud, and sabotage. Although the number of insider threats is much lower than external network attacks, insider threats can cause extensive damage. As insiders are very fami...