Title: Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams,
Abstract: Analysis of an organization's computer network activity is a key component of
early detection and mitigation of insider threat, a growing concern for many
organizations. Raw system logs are a prototypical example of streaming data
that can quickly scale beyond the cognitive power of a human analyst. As a
prospective filter for the human analyst, we present an online unsupervised
deep learning approach to detect anomalous network activity from system logs in
real time. Our models decompose anomaly scores into the contributions of
individual user behavior features for increased interpretability to aid
analysts reviewing potential cases of insider threat. Using the CERT Insider
Threat Dataset v6.2 and threat detection recall as our performance metric, our
novel deep and recurrent neural network models outperform Principal Component
Analysis, Support Vector Machine and Isolation Forest based anomaly detection
baselines. For our best model, the events labeled as insider threat activity in
our dataset had an average anomaly score in the 95.53 percentile, demonstrating
our approach's potential to greatly reduce analyst workloads.