NDSS

Forensic Analysis of Configuration-based Attacks

Muhammad Adil Inam (University of Illinois at Urbana-Champaign), Wajih Ul Hassan (University of Illinois at Urbana-Champaign), Ali Ahad (University of Virginia), Adam Bates (University of Illinois at Urbana-Champaign), Rashid Tahir (University of Prince Mugrin), Tianyin Xu (University of Illinois at Urbana-Champaign), Fareed Zaffar (LUMS)

Causality analysis is an effective technique for investigating and detecting cyber attacks. However, by focusing on auditing at the Operating System level, existing causal analysis techniques lack visibility into important application-level semantics, such as configuration changes that control application runtime behavior. This leads to incorrect attack attribution and half-baked tracebacks.

In this work, we propose Dossier, a specialized provenance tracker that enhances the visibility of the Linux auditing infrastructure. By providing additional hooks into the system, Dossier can generate a holistic view of the target application’s event history and causal chains, particularly those pertaining to configuration changes that are among the most common attack vectors observed in the real world. The extra “vantage points” in Dossier enable forensic investigators to bridge the semantic gap and correctly piece together attack fragments. Dossier leverages the versatility of information flow tracking and system call introspection to track all configuration changes, including both dynamic modifications that are applied directly to configuration-related program variables in memory and revisions to configuration files on disk with negligible runtime overhead (less than 7%). Evaluation on realistic workloads and real-world attack scenarios shows that Dossier can effectively reason about configuration-based attacks and accurately reconstruct the whole attack stories.