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The dynamics of mobile networks make it difficult for mobile apps to deliver a seamless user experience. In particular, intermittent connections and weak signals pose challenges for app developers. While recent network libraries have simplified network programming, much expert knowledge is still required. However, most mobile app developers are relative novices and tend to assume a reliable network connection, paying little attention to handling network errors in programming until users complain and leave bad reviews. We argue that the difficulty of avoiding such software defects can be mitigated through an annotation language that allows developers to declaratively state desired and actual properties of the application, largely without reference to fault-tolerant concepts, much less implementation. A pre-compiler can process these annotations, replacing calls to standard networking libraries with customized calls to a specialized library that enhances the reliability. This paper presents ANEL, a declarative language and middleware for Android that enables non-experts. We demonstrate the expressiveness and practicability of ANEL annotation through case studies and usability studies on real-world networked mobile apps. We also show that the ANEL middleware introduces negligible runtime performance overhead. 

Access control is often reported to be “profoundly broken” in real- world practices due to prevalent policy misconfigurations intro- duced by system administrators (sysadmins). Given the dynamics of resource and data sharing, access control policies need to be continuously updated. Unfortunately, to err is human—sysadmins often make mistakes such as over-granting privileges when chang- ing access control policies. With today’s limited tooling support for continuous validation, such mistakes can stay unnoticed for a long time until eventually being exploited by attackers, causing catastrophic security incidents. We present P-DIFF, a practical tool for monitoring access control behavior to help sysadmins early detect unintended access control policy changes and perform postmortem forensic analysis upon security attacks. P-DIFF continuously monitors access logs and infers access control policies from them. To handle the challenge of policy evolution, we devise a novel time-changing decision tree to effectively represent access control policy changes, coupled with a new learning algorithm to infer the tree from access logs. P-DIFF provides sysadmins with the inferred policies and detected changes to assist the following two tasks: (1) validating whether the access control changes are intended or not; (2) pinpointing the historical changes responsible for a given security attack. We evaluate P-DIFF with a variety of datasets collected from five real-world systems, including two from industrial companies. P- DIFF can detect 86%–100% of access control policy changes with an average precision of 89%. For forensic analysis, P-DIFF can pinpoint the root-cause change that permits the target access in 85%–98% of the evaluated cases.