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.

提出了一种基于无监督深度学习的在线异常检测方法，通过分解异常得分，帮助分析员识别潜在的内部威胁活动，实现了减轻分析员负担的目标。在 CERT Insider Threat Dataset v6.2 上的表现证明，该方法可以显著提高威胁检测的召回率。