Shiqing Ma, Purdue University; Juan Zhai, Nanjing University; Fei Wang, Purdue University; Kyu Hyung Lee, University of Georgia; Xiangyu Zhang and Dongyan Xu, Purdue University
Distinguished Paper Award Winner!
Traditional auditing techniques generate large and inaccurate causal graphs. To overcome such limitations, researchers proposed to leverage execution partitioning to improve analysis granularity and hence precision. However, these techniques rely on a low level programming paradigm (i.e., event handling loops) to partition execution, which often results in low level graphs with a lot of redundancy. This not only leads to space inefficiency and noises in causal graphs, but also makes it difficult to understand attack provenance. Moreover, these techniques require training to detect low level memory dependencies across partitions. Achieving correctness and completeness in the training is highly challenging. In this paper, we propose a semantics aware program annotation and instrumentation technique to partition execution based on the application specific high level task structures. It avoids training, generates execution partitions with rich semantic information and provides multiple perspectives of an attack. We develop a prototype and integrate it with three different provenance systems: the Linux Audit system, ProTracer and the LPM-HiFi system. The evaluation results show that our technique generates cleaner attack graphs with rich high-level semantics and has much lower space and time overheads, when compared with the event loop based partitioning techniques BEEP and ProTracer.
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