Insider Threat Detection Model Enhancement Using Hybrid Algorithms between Unsupervised and Supervised Learning
Junkai Yi,
Yongbo Tian
Abstract:Insider threats are one of the most costly and difficult types of attacks to detect due to the fact that insiders have the right to access an organization’s network systems and understand its structure and security procedures, making it difficult to detect this type of behavior through traditional behavioral auditing. This paper proposes a method to leverage unsupervised outlier scores to enhance supervised insider threat detection by integrating the advantages of supervised and unsupervised learning methods a… Show more
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