ProTracer: Towards Practical Provenance Tracing by Alternating Between Logging and Tainting
Shiqing Ma, Xiangyu Zhang, Dongyan Xu
Abstract
Provenance tracing is a very important approach to Advanced Persistent Threat (APT) attack detection and investigation. Existing techniques either suffer from the dependence explosion problem or have non-trivial space and runtime overhead, which hinder their application in practice. We propose ProTracer, a lightweight provenance tracing system that alternates between system event logging and unit level taint propagation. The technique is built on an on-the-fly system event processing infrastructure that features a very lightweight kernel module and a sophisticated user space daemon that performs concurrent and out-of-order event processing. The evaluation with different realistic system workloads and a number of attack cases show that ProTracer only produces 13MB log data per day, and 0.84GB(Server)/2.32GB(Client) in 3 months without losing any important information. The space consumption is only < 1.28% of the state-of-the-art, 7 times smaller than an off-line garbage collection technique. The run-time overhead averages <7% for servers and <5% for regular applications. The generated attack causal graphs are a few times smaller than those by existing techniques while they are equally informative. Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers75
- HOLMES: Real-Time APT Detection through Correlation of Suspicious Information FlowsSadegh Momeni Milajerdi, Rigel Gjomemo, Birhanu Eshete, R. Sekar et al.S&P 2019 · 550 citations
- NoDoze: Combatting Threat Alert Fatigue with Automated Provenance TriageWajih Ul Hassan, Shengjian Guo, Ding Li, Zhengzhang Chen et al.NDSS 2019 · 411 citations
- Tactical Provenance Analysis for Endpoint Detection and Response SystemsWajih Ul Hassan, Adam Bates, Daniel MarinoS&P 2020 · 317 citations
- POIROT: Aligning Attack Behavior with Kernel Audit Records for Cyber Threat HuntingSadegh M. Milajerdi, Birhanu Eshete, Rigel Gjomemo, V. N. VenkatakrishnanCCS 2019 · 313 citations
- SLEUTH: Real-time Attack Scenario Reconstruction from COTS Audit DataMd Nahid Hossain, Sadegh M. Milajerdi, Junao Wang, Birhanu Eshete et al.USENIX Security 2017 · 291 citations
Related papers
- DISTDET: A Cost-Effective Distributed Cyber Threat Detection SystemFeng Dong, Liu Wang, Xu Nie, Fei Shao et al.USENIX Security 2023
- Towards a Timely Causality Analysis for Enterprise SecurityYushan Liu, Mu Zhang, Ding Li, Kangkook Jee et al.NDSS 2018 · 177 citations
- Clearing the Clutter: Real-Time Program-Specific Log Consolidation for APT DetectionXiao Han, Jiahao Xue, Zhuo Lu, Yao LiuINFOCOM 2026
- PROGRAPHER: An Anomaly Detection System based on Provenance Graph EmbeddingFan Yang, Jiacen Xu, Chunlin Xiong, Zhou Li et al.USENIX Security 2023
- Brewing Vodka: Distilling Pure Knowledge for Lightweight Threat Detection in Audit LogsWeiheng Wu, Wei Qiao, Wenhao Yan, Bo Jiang et al.WWW 2025 · 4 citations
