Mnemosyne: An Effective and Efficient Postmortem Watering Hole Attack Investigation System
Joey Allen, Zheng Yang, Matthew Landen, Raghav Bhat, Harsh Grover, Andrew Chang, Yang Ji, Roberto Perdisci, Wenke Lee
摘要
Compromising a website that is routinely visited by employees of a targeted organization has become a popular technique for nation-state level adversaries to penetrate an enterprise's network. This technique, dubbed a "watering hole" attack, leverages a compromised website to serve as a stepping stone into the true victims' network. Despite watering hole attacks being one of the main techniques used by attackers to achieve the initial compromise stage of the cyber kill chain, there has been relatively little research related to detecting or investigating complex watering hole attacks. While there is existing work that seeks to detect malicious modifications made to an otherwise benign website, we argue that simply detecting that the website is compromised is only the first stage of the investigation. In this paper, we propose Mnemosyne, a postmortem forensic analysis engine that relies on browser-based attack provenance to accurately reconstruct, investigate, and assess the ramifications of watering hole attacks. Mnemosyne relies on a lightweight browser-modification-free auditing daemon to passively collect causality logs related to the browser's execution. Next, Mnemosyne applies a set of versioning techniques on top of these causality logs to precisely pinpoint when the website was compromised and what modifications were made by the adversary. Following this step, Mnemosyne relies on a novel user-level analysis to assess how the malicious modifications affected the targeted enterprise and seeks to identify exactly which employees fell victim to the attack. Throughout our extensive evaluation, we found that Mnemosyne's forensic analysis engine was able to identify the true victims in all seven real-world watering hole scenarios, while also reducing the amount of manual analysis required by the forensic analyst by 98.17% on average.
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引用它的顶会 Paper4
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- Lock the Door But Keep the Window Open: Extracting App-Protected Accessibility Information from Browser-Rendered WebsitesHaichuan Xu, Runze Zhang, Mingxuan Yao, David Oygenblik 等CCS 2025
- TRIDENT: Towards Detecting and Mitigating Web-based Social Engineering AttacksZheng Yang, Joey Allen, Matthew Landen, Roberto Perdisci 等USENIX Security 2023
- TeSec: Accurate Server-side Attack Investigation for Web ApplicationsRuihua Wang, Yihao Peng, Yilun Sun, Xuancheng Zhang 等S&P 2023
它引用的顶会 Paper17
- NoDoze: Combatting Threat Alert Fatigue with Automated Provenance TriageWajih Ul Hassan, Shengjian Guo, Ding Li, Zhengzhang Chen 等NDSS 2019 · 被引用 411 次
- Tactical Provenance Analysis for Endpoint Detection and Response SystemsWajih Ul Hassan, Adam Bates, Daniel MarinoS&P 2020 · 被引用 317 次
- SLEUTH: Real-time Attack Scenario Reconstruction from COTS Audit DataMd Nahid Hossain, Sadegh M. Milajerdi, Junao Wang, Birhanu Eshete 等USENIX Security 2017 · 被引用 291 次
- ProTracer: Towards Practical Provenance Tracing by Alternating Between Logging and TaintingShiqing Ma, Xiangyu Zhang, Dongyan XuNDSS 2016 · 被引用 253 次
- Fear and Logging in the Internet of ThingsQi Wang, Wajih Ul Hassan, Adam Bates, Carl A. GunterNDSS 2018 · 被引用 205 次
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