Contextual feature representation for image-based insider threat classification

With the rapid development of technology, insider threat incidents frequently occur in organizations. Detecting insider threats is an essential task in network infrastructure security. In this paper, we design an attention module to extract contextual features and augment abnormal features to generate high-quality images representing user behavior. Then, we use pre-trained ResNet and multi-source feature fusion on behavioral, psychological, and role features, intending to identify malicious insiders accurately. The proposed approaches are evaluated using the CMU-CERT Insider Threat Dataset. Experimental results show the effectiveness of methods and outperform other state-of-the-art methods.