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S&P2023顶会

TeSec: Accurate Server-side Attack Investigation for Web Applications

Ruihua Wang, Yihao Peng, Yilun Sun, Xuancheng Zhang, Hai Wan, Xibin Zhao

2023年份
3顶会引用

摘要

The user interface (UI) of web applications is usually the entry point of web attacks against enterprises and organizations. Finding the UI elements utilized by the intruders is of great importance both for attack interception and web application fixing. Current attack investigation methods targeting web UI either provide rough analysis results or have poor performance in high concurrency scenarios, which leads to heavy manual analysis work. In this paper, we propose TeSec, an accurate attack investigation method for web UI applications. TeSec makes use of two kinds of correlations. The first one, built from annotated audit log partitioned by PID/TID and delimiter-logs, captures the correspondence between audit log entries and web requests. The second one, modeled by an Aho-Corasick automaton built during system testing period, captures the correspondence between requests and the UI elements/events. Leveraging these two correlations, TeSec can accurately and automatically locate the UI elements/events (i.e., the root cause of the alarm) from an alarm, even in high concurrency scenarios. Furthermore, TeSec only needs to be deployed in the server and does not need to collect logs from the client-side browsers. We evaluate TeSec on 12 web applications. The experimental results show that the matching accuracy between UI events/elements and the alarm is above 99.6%. And security analysts only need to check no more than 2 UI elements on average for each individual forensics analysis. The maximum overhead of average response time and audit log space overhead are low (4.3% and 4.6% respectively).

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