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System auditing is a central concern when investigating and responding to security incidents. Unfortunately, attackers regularly engage in anti-forensic activities after a break-in, covering their tracks from the system logs in order to frustrate the efforts of investigators. While a variety of tamper-evident logging solutions have appeared throughout the industry and the literature, these techniques do not meet the operational and scalability requirements of system-layer audit frameworks. In this work, we introduce Custos, a practical framework for the detection of tampering in system logs. Custos consists of a tamper-evident logging layer and a decentralized auditing protocol. The former enables the verification of log integrity with minimal changes to the underlying logging framework, while the latter enables near real-time detection of log integrity violations within an enterprise-class network. Custos is made practical by the observation that we can decouple the costs of cryptographic log commitments from the act of creating and storing log events, without trading off security, leveraging features of off-the-shelf trusted execution environments. Supporting over one million events per second, we show that Custos' tamper-evident logging protocol is three orders of magnitude (1000×) faster than prior solutions and incurs only between 2% and 7% runtime overhead over insecure logging on intensive workloads. Further, we show that Custos' auditing protocol can detect violations in near real-time even in the presence of a powerful distributed adversary and with minimal (3%) network overhead. Our case study on a real-world APT attack scenario demonstrates that Custos forces anti-forensic attackers into a "lose-lose" situation, where they can either be covert and not tamper with logs (which can be used for forensics), or erase logs but then be detected by Custos.
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On the Forensic Validity of Approximated Audit Logs
Auditing is an increasingly essential tool for the defense of computing systems, but the unwieldy nature of log data imposes significant burdens on administrators and analysts. To address this issue, a variety of techniques have been proposed for approximating the contents of raw audit logs, facilitating efficient storage and analysis. However, the security value of these approximated logs is difficult to measure—relative to the original log, it is unclear if these techniques retain the forensic evidence needed to effectively investigate threats. Unfortunately, prior work has only investigated this issue anecdotally, demonstrating sufficient evidence is retained for specific attack scenarios. In this work, we address this gap in the literature through formalizing metrics for quantifying the forensic validity of an approximated audit log under differing threat models. In addition to providing quantifiable security arguments for prior work, we also identify a novel point in the approximation design space—that log events describing typical (benign) system activity can be aggressively approximated, while events that encode anomalous behavior should be preserved with lossless fidelity. We instantiate this notion of Attack-Preserving forensic validity in LogApprox, a new approximation technique that eliminates the redundancy of voluminous file I/O associated with benign process activities. We evaluate LogApprox alongside a corpus of exemplar approximation techniques from prior work and demonstrate that LogApprox achieves comparable log reduction rates while retaining 100% of attack-identifying log events. Additionally, we utilize this evaluation to illuminate the inherent trade-off between performance and utility within existing approximation techniques. This work thus establishes trustworthy foundations for the design of the next generation of efficient auditing frameworks.
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- Award ID(s):
- 1750024
- PAR ID:
- 10232054
- Date Published:
- Journal Name:
- Annual Computer Security Applications Conference
- Page Range / eLocation ID:
- 189 to 202
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3427228.3427272
