MITD: A Multi-Dimensional Insider Threat Detection Framework Based On Deep Learning

In this paper, we propose a multi-dimensional insider threat detection framework (MITD), which aims to improve the accuracy of detection by comprehensively analyzing user behavior patterns. We combine statistical features and behavioral sequences and use sequence pattern recognition, graph structure information mining, and adaptive feature extraction methods. It can reveal user behavior from multiple dimensions. Then, we intelligently fuse the extracted multi-dimensional features to generate a comprehensive feature representation. It is further fused with statistical features through two strategies: concat and bitwise addition. Experimental results based on the CERT r4.2 dataset show that the MITD framework significantly improves in terms of accuracy, precision, recall, and F1 score, reaching 93.2% accuracy and 93.5% recall. The results show that the MITD framework effectively improves the performance of insider threat detection and has great potential in practical applications.