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

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...

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Veröffentlicht in:Applied sciences Jg. 9; H. 19; S. 4018
Hauptverfasser: Kim, Junhong, Park, Minsik, Kim, Haedong, Cho, Suhyoun, Kang, Pilsung
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2019
Schlagworte:
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
ISSN:2076-3417, 2076-3417
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app9194018