内部威胁
计算机科学
知情人
水准点(测量)
审计
计算机安全
机器学习
人工智能
作者
M. S. Vinay,Shuhan Yuan,Xintao Wu
标识
DOI:10.1007/978-3-031-00123-9_32
摘要
Insider threat detection techniques typically employ supervised learning models for detecting malicious insiders by using insider activity audit data. In many situations, the number of detected malicious insiders is extremely limited. To address this issue, we present a contrastive learning-based insider threat detection framework, CLDet, and empirically evaluate its efficacy in detecting malicious sessions that contain malicious activities from insiders. We evaluate our framework along with state-of-the-art baselines on two unbalanced benchmark datasets. Our framework exhibits relatively superior performance on these unbalanced datasets in effectively detecting malicious sessions. KeywordsInsider threat detectionContrastive learningCyber-security
科研通智能强力驱动
Strongly Powered by AbleSci AI