UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats (2001.01525v2)

Abstract: Advanced Persistent Threats (APTs) are difficult to detect due to their "low-and-slow" attack patterns and frequent use of zero-day exploits. We present UNICORN, an anomaly-based APT detector that effectively leverages data provenance analysis. From modeling to detection, UNICORN tailors its design specifically for the unique characteristics of APTs. Through extensive yet time-efficient graph analysis, UNICORN explores provenance graphs that provide rich contextual and historical information to identify stealthy anomalous activities without pre-defined attack signatures. Using a graph sketching technique, it summarizes long-running system execution with space efficiency to combat slow-acting attacks that take place over a long time span. UNICORN further improves its detection capability using a novel modeling approach to understand long-term behavior as the system evolves. Our evaluation shows that UNICORN outperforms an existing state-of-the-art APT detection system and detects real-life APT scenarios with high accuracy.

• The paper introduces Unicorn, a novel runtime provenance-based detector that identifies advanced persistent threats by modeling system behavior with continuous graph analysis.
• It employs directed acyclic provenance graphs and a modified Weisfeiler-Lehman algorithm to transform long execution traces into compact, analyzable sketches.
• Empirical results demonstrate Unicorn’s effectiveness, achieving 24% higher precision and 30% improved accuracy compared to traditional detection systems.

An Analysis of Unicorn: A Runtime Provenance-Based Detector for Advanced Persistent Threats

The prevalence of Advanced Persistent Threats (APTs) in the cybersecurity landscape has necessitated the development of more sophisticated detection mechanisms. Traditional detection systems, which primarily rely on signature-based methods, struggle to effectively identify APTs due to their characteristic "low-and-slow" attack patterns and reliance on zero-day exploits. This paper introduces "Unicorn," a novel anomaly-based detection system that leverages runtime data provenance analysis to address the challenges posed by APTs.

Overview of Unicorn's Methodology

Unicorn's architecture is designed to dynamically model and detect anomalous system behaviors characteristic of APTs. Fundamentally, Unicorn departs from static models that capture a singular snapshot of system execution, instead opting for a continuous provenance-based graph analysis.

1. Provenance Graphs: Unicorn constructs directed acyclic graphs (DAGs) to represent system executions, capturing complex causal relationships over time. These provenance graphs are pivotal in identifying temporal and contextual anomalies that differentiate benign from malicious behaviors.
2. Graph Histogram and Sketch: At the heart of Unicorn's approach is the transformation of lengthy provenance graphs into succinct graph sketches. This involves creating a histogram of vertex label distributions through a modified Weisfeiler-Lehman algorithm, which iteratively enlarges neighborhood analysis to capture extensive contextual information.
3. Incremental Sketch Update: The graph sketches are incrementally updated to adapt to the evolving system behavior, making Unicorn capable of efficiently processing streaming data in practice. This ensures that the size of each sketch remains fixed and computationally manageable.
4. Evolutionary Modeling and Detection: Unicorn incorporates these sketches into an evolutionary model using clustering techniques to categorize different states of system behavior over time. This model recognizes legitimate state transitions while identifying potential anomalies based on deviations from trained patterns.

Empirical Evaluation

The efficacy of Unicorn was evaluated against both simulated and real attack scenarios, demonstrating its capability to outperform existing systems, with improvements in precision and accuracy by 24% and 30%, respectively. The evaluation was comprehensive, including DARPA datasets across various operating systems and user environments, underscoring Unicorn's adaptability and generalization capability.

Implications and Future Directions

The practical implications of Unicorn are noteworthy for cybersecurity practices, especially in environments susceptible to APT attacks. By focusing on provenance data, Unicorn benefits from capturing long-term and contextual system behaviors, which are often subverted in APTs. The system's architectural emphasis on evolutionary modeling without necessitating runtime model updates provides resilience against APT infiltration strategies designed to gradually adapt to detection thresholds.

Future developments could explore a more nuanced approach to parameter tuning, potentially integrating adaptive mechanisms to balance detection flexibility with stability. Additionally, further exploration of graph-based anomaly detection principles could enhance Unicorn’s efficiency, particularly in more heterogeneous operating environments such as individual workstations.

Conclusion

Unicorn stands as a robust advancement in the detection of APTs, illustrating the efficacy of runtime provenance data analysis in cybersecurity. Its sophisticated use of graph-based techniques and scalable, real-time approach positions it as a notable contribution to safeguarding systems against increasingly sophisticated cyber threats.