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- Title
Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms.
- Authors
Kim, Junhong; Park, Minsik; Kim, Haedong; Cho, Suhyoun; Kang, Pilsung
- Abstract
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 familiar with an organization's system, it is very difficult to detect their malicious behavior. Traditional insider-threat detection methods focus on rule-based approaches built by domain experts, but they are neither flexible nor robust. In this paper, we propose insider-threat detection methods based on user behavior modeling and anomaly detection algorithms. Based on user log data, we constructed three types of datasets: user's daily activity summary, e-mail contents topic distribution, and user's weekly e-mail communication history. Then, we applied four anomaly detection algorithms and their combinations to detect malicious activities. Experimental results indicate that the proposed framework can work well for imbalanced datasets in which there are only a few insider threats and where no domain experts' knowledge is provided.
- Subjects
ANOMALY detection (Computer security); INTRUSION detection systems (Computer security); HUMAN behavior models; INTELLECTUAL property theft; INFORMATION technology security; EMAIL; USER-generated content
- Publication
Applied Sciences (2076-3417), 2019, Vol 9, Issue 19, p4018
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app9194018