System logs are invaluable to forensic audits, but grow so large that in practice fine-grained logs are quickly discarded – if captured at all – preventing the real-world use of the provenance-based investigation techniques that have gained popularity in the literature. Encouragingly, forensically-informed methods for reducing the size of system logs are a subject of frequent study. Unfortunately, many of these techniques are designed for offline reduction in a central server, meaning that the up-front cost of log capture, storage, and transmission must still be paid at the endpoints. Moreover, to date these techniques exist as isolated (and, often, closed-source) implementations; there does not exist a comprehensive framework through which the combined benefits of multiple log reduction techniques can be enjoyed.
In this work, we present FAuST, an audit daemon for performing streaming audit log reduction at system endpoints. After registering with a log source (e.g., via Linux Audit’s audisp utility), FAuST incrementally builds an in-memory provenance graph of recent system activity. During graph construction, log reduction techniques that can be applied to local subgraphs are invoked immediately using event callback handlers, while techniques meant for application on the global graph are invoked in periodic epochs. We evaluate FAuST, loaded with eight different log reduction modules from the literature, against the DARPA Transparent Computing datasets. Our experiments demonstrate the efficient performance of FAuST and identify certain subsets of reduction techniques that are synergistic with one another. Thus, FAuST dramatically simplifies the evaluation and deployment of log reduction techniques.
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This content will become publicly available on May 1, 2024
SoK: History is a Vast Early Warning System: Auditing the Provenance of System Intrusions
Auditing, a central pillar of operating system security, has only recently come into its own as an active area of public research. This resurgent interest is due in large part to the notion of data provenance, a technique that iteratively parses audit log entries into a dependency graph that explains the history of system execution. Provenance facilitates precise threat detection and investigation through causal analysis of sophisticated intrusion behaviors. However, the absence of a foundational audit literature, combined with the rapid publication of recent findings, makes it difficult to gain a holistic picture of advancements and open challenges in the area.In this work, we survey and categorize the provenance-based system auditing literature, distilling contributions into a layered taxonomy based on the audit log capture and analysis pipeline. Recognizing that the Reduction Layer remains a key obstacle to the further proliferation of causal analysis technologies, we delve further on this issue by conducting an ambitious independent evaluation of 8 exemplar reduction techniques against the recently-released DARPA Transparent Computing datasets. Our experiments uncover that past approaches frequently prune an overlapping set of activities from audit logs, reducing the synergistic benefits from applying them in tandem; further, we observe an inverse relation between storage efficiency and anomaly detection performance. However, we also observe that log reduction techniques are able to synergize effectively with data compression, potentially reducing log retention costs by multiple orders of magnitude. We conclude by discussing promising future directions for the field.
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- NSF-PAR ID:
- 10443648
- Date Published:
- Journal Name:
- 2023 IEEE Symposium on Security and Privacy (SP)
- Page Range / eLocation ID:
- 2620 to 2638
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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