Enabling Reconstruction of Attacks on Users via Efficient Browsing Snapshots
Phani Vadrevu, Jienan Liu, Bo Li, Babak Rahbarinia, Kyu Hyung Lee, Roberto Perdisci
摘要
In this paper, we present ChromePic, a web browser equipped with a novel forensic engine that aims to greatly enhance the browser's logging capabilities. ChromePic's main goal is to enable a fine-grained post-mortem reconstruction and trace-back of web attacks without incurring the high overhead of record-andreplay systems. In particular, we aim to enable the reconstruction of attacks that target users and have a significant visual component, such as social engineering and phishing attacks. To this end, ChromePic records a detailed snapshot of the state of a web page, including a screenshot of how the page is rendered and a "deep" DOM snapshot, at every significant interaction between the user and the page. If an attack is later suspected, these finegrained logs can be used to reconstruct the attack and trace back the sequence of steps the user followed to reach the attack page. We develop ChromePic by implementing several careful modifications and optimizations to the Chromium code base, to minimize overhead and make always-on logging practical. We then demonstrate that ChromePic can successfully capture and aid the reconstruction of attacks on users. Our evaluation includes the analysis of an in-the-wild social engineering download attack on Android, a phishing attack, and two different clickjacking attacks, as well as a user study aimed at accurately measuring the overhead introduced by our forensic engine. The experimental results show that browsing snapshots can be logged very efficiently, making the logging events practically unnoticeable to users. 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.
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引用它的顶会 Paper9
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- Pride and Prejudice in Progressive Web Apps: Abusing Native App-like Features in Web ApplicationsJiyeon Lee, Hayeon Kim, Junghwan Park, Insik Shin 等CCS 2018 · 被引用 40 次
- Mnemosyne: An Effective and Efficient Postmortem Watering Hole Attack Investigation SystemJoey Allen, Zheng Yang, Matthew Landen, Raghav Bhat 等CCS 2020 · 被引用 14 次
它引用的顶会 Paper4
- ProTracer: Towards Practical Provenance Tracing by Alternating Between Logging and TaintingShiqing Ma, Xiangyu Zhang, Dongyan XuNDSS 2016 · 被引用 253 次
- Investigating Commercial Pay-Per-Install and the Distribution of Unwanted SoftwareKurt Thomas, Juan A. Elices Crespo, Ryan Rasti, Jean-Michel Picod 等USENIX Security 2016 · 被引用 77 次
- Measuring PUP Prevalence and PUP Distribution through Pay-Per-Install ServicesPlaton Kotzias, Leyla Bilge, Juan CaballeroUSENIX Security 2016 · 被引用 74 次
- Towards Measuring and Mitigating Social Engineering Software Download AttacksTerry Nelms, Roberto Perdisci, Manos Antonakakis, Mustaque AhamadUSENIX Security 2016 · 被引用 70 次
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