Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams (1710.00811v2)

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.

• The paper proposes an unsupervised deep learning approach using DNNs and RNNs to dynamically detect anomalies in structured cybersecurity data streams.
• It demonstrates how context-aware models decompose anomaly scores into clear user behavior features, achieving insider threat events at the 95.53 percentile.
• The approach reduces analyst workload by isolating high-risk events and paves the way for more granular real-time user behavior analysis.

An Examination of "Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams"

The role of deep learning experiences new momentum with the exploration of unsupervised models for insider threat detection in cybersecurity data streams. This paper proposes and evaluates an unsupervised deep learning approach using Deep Neural Networks (DNNs) and Recurrent Neural Networks (RNNs) aimed at detecting anomalies indicative of insider threats. Such threats are increasingly complex to identify due to their iterative and multifaceted nature. Here, the authors delve into an innovative approach that prioritizes real-time detection and model interpretability, tackling the significant challenge presented by the sheer volume and varied nature of cybersecurity data streams.

Core Model Insights

The authors use system logs generated by computer network activities as inputs for these models. DNNs and RNNs are employed to recognize and contextualize user behavior, thereby identifying discrepancies from standard behaviors that might indicate insider threats. The models are trained in an online manner, adapting dynamically to the data as it's received, ensuring scalability and immediate application. A notable highlight is the model's capability to break down the anomaly detection scores into discrete user behavior features. This decomposition enhances the interpretability—facilitating analysts' comprehension of potentially threatening activities, exemplified by accomplishing tasks such as identifying file transfers during uncommon hours.

Performance Benchmarks

The evaluation employs the CERT Insider Threat Dataset v6.2, utilizing key performance measures including threat detection recall. The presented models, particularly those incorporating diagonal covariance, surpass traditional methods like Principal Component Analysis (PCA), Support Vector Machine (SVM), and Isolation Forest. The achievement, illustrated by the high anomaly scores of insider threat events reaching the 95.53 percentile, indicates substantive reductions in workload for human analysts, as only a fraction of events require detailed human evaluation.

Implications and Prospects

This research advocates for a fundamental shift in addressing insider threats, proposing abstraction away from explicitly modeling threat types towards profiling "normal" behavior against which threat behavior is compared. The inherent advantage is the ability to respond to the ever-evolving landscape of insider threats. Moreover, the paper highlights the success of DNNs utilizing in-stream diagonal covariance matrices, which enable context-aware anomaly assessments that outperform fixed assumptions inherent in identity covariances.

From a practical standpoint, this research holds considerable potential in reducing alert fatigue among cybersecurity analysts by streamlining operational workflows and spotlighting high-risk anomalies with succinct rationale. Theoretical advances implicit in the application of state-sharing mechanisms across user instances in RNNs open fruitful avenues for further enhancement in model efficacy and computational resource efficiency.

Future Directions

Future exploration should focus on applying this unsupervised approach to authentic and diverse datasets encompassing more nuanced user behavior patterns that challenge current anomaly models. Additionally, considering the transition to finer temporal resolution or real-time granular event analysis could extract more value from continuous user-log streams, potentially enabling higher detection accuracy with even lower false-positive rates. Ultimately, while the models and techniques discussed provide robust baselines and insights, continuous evolution through the fusion of advanced learning techniques and domain-specific knowledge will be critical to further progress in cybersecurity measures.

This paper constitutes a pivotal contribution to the field of cybersecurity, underscoring the adaptability of deep learning frameworks to complex and dynamic environments where traditional methods face substantive limitations.