Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events

With the continuous advancement of enterprise digitization, insider threats have become one of the primary cybersecurity concerns for organizations. Therefore, it is of great significance to develop an effective insider threat detection mechanism to ensure the security of enterprises. Most methods rely on artificial feature engineering and input the extracted user behavior features into a clustering-based unsupervised machine learning model for insider threat detection. However, feature extraction is independent of clustering-based unsupervised machine learning. As a result, user behavior features are not the most appropriate for clustering-based unsupervised machine learning, and thus, they reduce the insider threat detection accuracy. This paper proposes an insider threat detection method based on the deep clustering of multi-source behavioral events. On the one hand, the proposed method constructs an end-to-end deep clustering network and automatically learns the user behavior feature expression from multi-source behavioral event sequences. On the other hand, a deep clustering objective function is presented to jointly optimize the learning of feature representations and the clustering task for insider threat detection. This optimization can adjust the optimal user behavior features for the clustering model to improve the insider threat detection accuracy. The experimental results show that the proposed end-to-end insider threat detection model can accurately identify insider threats based on abnormal multi-source user behaviors in enterprise networks.