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Title: Runtime Recovery of Web Applications under Zero-Day ReDoS Attacks
Regular expression denial of service (ReDoS)— which exploits the super-linear running time of matching regular expressions against carefully crafted inputs—is an emerging class of DoS attacks to web services. One challenging question for a victim web service under ReDoS attacks is how to quickly recover its normal operation after ReDoS attacks, especially these zero-day ones exploiting previously unknown vulnerabilities.In this paper, we present RegexNet, the first payload-based, automated, reactive ReDoS recovery system for web services. RegexNet adopts a learning model, which is updated constantly in a feedback loop during runtime, to classify payloads of upcoming requests including the request contents and database query responses. If detected as a cause leading to ReDoS, RegexNet migrates those requests to a sandbox and isolates their execution for a fast, first-measure recovery.We have implemented a RegexNet prototype and integrated it with HAProxy and Node.js. Evaluation results show that RegexNet is effective in recovering the performance of web services against zero-day ReDoS attacks, responsive on reacting to attacks in sub-minute, and resilient to different ReDoS attack types including adaptive ones that are designed to evade RegexNet on purpose.  more » « less
Award ID(s):
1918757
NSF-PAR ID:
10341118
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
2021 IEEE Symposium on Security and Privacy (SP)
Page Range / eLocation ID:
1575 to 1588
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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