Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms
- Authors
- Kim, Junhong; Park, Minsik; Kim, Haedong; Cho, Suhyoun; Kang, Pilsung
- Issue Date
- Oct-2019
- Publisher
- MDPI
- Keywords
- insider threat detection; anomaly detection; machine learning; behavioral model; latent dirichlet allocation; e-mail network
- Citation
- APPLIED SCIENCES-BASEL, v.9, no.19
- Indexed
- SCIE
SCOPUS
- Journal Title
- APPLIED SCIENCES-BASEL
- Volume
- 9
- Number
- 19
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/62622
- DOI
- 10.3390/app9194018
- ISSN
- 2076-3417
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholar.korea.ac.kr/handle/2021.sw.korea/62622)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.