RAIN

Yang Ji, Sangho Lee, Evan Downing, Weiren Wang, Mattia Fazzini, Taesoo Kim, Alessandro Orso, Wenke Lee
2017 Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security - CCS '17  
As modern attacks become more stealthy and persistent, detecting or preventing them at their early stages becomes virtually impossible. Instead, an attack investigation or provenance system aims to continuously monitor and log interesting system events with minimal overhead. Later, if the system observes any anomalous behavior, it analyzes the log to identify who initiated the attack and which resources were affected by the attack and then assess and recover from any damage incurred. However,
more » ... cause of a fundamental tradeoff between log granularity and system performance, existing systems typically record system-call events without detailed program-level activities (e.g., memory operation) required for accurately reconstructing attack causality or demand that every monitored program be instrumented to provide program-level information. To address this issue, we propose Rain, a Refinable Attack INvestigation system based on a record-replay technology that records system-call events during runtime and performs instructionlevel dynamic information flow tracking (DIFT) during on-demand process replay. Instead of replaying every process with DIFT, Rain conducts system-call-level reachability analysis to filter out unrelated processes and to minimize the number of processes to be replayed, making inter-process DIFT feasible. Evaluation results show that Rain effectively prunes out unrelated processes and determines attack causality with negligible false positive rates. In addition, the runtime overhead of Rain is similar to existing systemcall level provenance systems and its analysis overhead is much smaller than full-system DIFT.
doi:10.1145/3133956.3134045 dblp:conf/ccs/JiLDWFKOL17 fatcat:brdz6ihitjgpjksymswhbirebm