Memory-Augmented Insider Threat Detection with Temporal-Spatial Fusion

Insider threat detection is important for the smooth operation and security protection of an organizational system. Most existing detection models establish historical baseline by reconstructing single-day and individual user behaviors, and then treat any outlier of the baseline as a threat. However, such methods ignore the temporal and spatial correlations between different activities, which result in an unsatisfying performance. To address such an issue, we propose a novel insider threat detection method, namely, Memory-Augmented Insider Threat Detection (MAITD), in this paper. Such an idea is motivated by the observation that the combination of individual model that focuses on historical baseline and group model that represents peer baseline can effectively identify the low-signal yet long-lasting insider threats, and reduce the possibility of false positives. To illustrate, our MAITD captures the temporal and spatial correlation of user behaviors by constructing compound behavioral matrix and common group model, and combines specific application scenarios to integrate the detection results. Moreover, it introduces the memory-augmented network into autoencoder to enlarge the reconstruction error of abnormal samples, thereby reducing the false negative rate. The experimental results on CERT dataset show that the instance-based and user-based AUCs of MAITD reach up to 87.54% and 94.56%, respectively, which significantly outperform previous works.