Applying Provenance in APT Monitoring and Analysis: Practical Challenges for Scalable, Efficient and Trustworthy Distributed Provenance

Advanced Persistent Threats (APT) are a class of security threats in which a well-resourced attacker targets a specific individual or organisation with a predefined goal. This typically involves exfiltration of confidential material, although increasingly attacks target the encryption or destruction of mission critical data. With traditional prevention and detection mechanisms failing to stem the tide of such attacks, there is a pressing need for new monitoring and analysis tools that reduce both false-positive rates and the cognitive burden on human analysts.

We propose that local and distributed provenance metadata can simplify and improve monitoring and analysis of APTs by providing a single, authoritative sequence of events that captures the context (and side effects) of potentially malicious activities. Provenance metadata allows a human analyst to backtrack from detection of malicious activity to the point of intrusion and, similarly, to work forward to fully understand the consequences. Applying provenance to APT monitoring and analysis introduces some significantly different challenges and requirements in comparison to more traditional applications. Drawing from our experiences working with and adapting the OPUS (Observed Provenance in User Space) system to an APT monitoring and analysis use case, we introduce and discuss some of the key challenges in this space. These preliminary observations are intended to prime a discussion within the community about the design space for scalable, efficient and trustworthy distributed provenance for scenarios that impose different constraints from traditional provenance applications such as workflow and data processing frameworks.