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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. 

Permission-based access control enables users to manage and control their sensitive data for third-party applications. In an ideal scenario, third-party application includes enough details to illustrate the usage of such data, while the reality is that many descriptions of third-party applications are vague about their security or privacy activities. As a result, users are left with insufficient details when granting sensitive data to these applications. Prior works, such as WHYPER and AutoCog, have addressed the aforementioned problem via a so-called permission correlation system. Such a system correlates third-party applications' description with their requested permissions and determines an application as overprivileged if a mismatch is found. However, although prior works are successful on their own platforms, such as Android eco-system, they are not directly applicable to new platforms, such as Chrome extensions and IFTTT, without extensive data labeling and parameter tuning. In this paper, we design, implement, and evaluate a novel system, called TKPERM, which transfers knowledges of permission correlation systems across platforms. Our key idea is that these varied platforms with different use cases---like smartphones, IoTs, and desktop browsers---are all user-facing and thus allow the knowledges to be transferrable across platforms. Particularly, we adopt a greedy selection algorithm that picks the best source domains to transfer to the target permission on a new platform. TKPERM achieves 90.02% overall F1 score after transfer, which is 12.62% higher than the one of a model trained directly on the target domain without transfer. Particularly, TKPERM has 91.83% F1 score on IFTTT, 89.13% F1 score on Chrome-Extension, and 89.1% F1 score on SmartThings. TKPERM also successfully identified many real-world overprivileged applications, such as a gaming hub requesting location permissions without legitimate use.