| Title | Insider Threat Detection Using An Unsupervised Learning Method: COPOD |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Sun, Xiaoshuang, Wang, Yu, Shi, Zengkai |
| Conference Name | 2021 International Conference on Communications, Information System and Computer Engineering (CISCE) |
| Keywords | anomaly detection, Communication networks, Companies, composability, feature extraction, Forestry, Human Behavior, insider threat, Insider Threat Detection, Intrusion detection, Metrics, policy-based governance, pubcrawl, security, tree structure analysis, unsupervised learning, Vegetation |
| Abstract | In recent years, insider threat incidents and losses of companies or organizations are on the rise, and internal network security is facing great challenges. Traditional intrusion detection methods cannot identify malicious behaviors of insiders. As an effective method, insider threat detection technology has been widely concerned and studied. In this paper, we use the tree structure method to analyze user behavior, form feature sequences, and combine the Copula Based Outlier Detection (COPOD) method to detect the difference between feature sequences and identify abnormal users. We experimented on the insider threat dataset CERT-IT and compared it with common methods such as Isolation Forest. |
| DOI | 10.1109/CISCE52179.2021.9445898 |
| Citation Key | sun_insider_2021 |
Insider Threat Detection Using An Unsupervised Learning Method: COPOD