Abstract

Online social networks are plagued by fake information. In particu- lar, using massive fake accounts (also called Sybils), an attacker can disrupt the security and privacy of benign users by spreading spam, malware, and disinformation. Existing Sybil detection methods rely on rich content, behavior, and/or social graphs generated by Sybils. The key limitation of these methods is that they incur significant delays in catching Sybils, i.e., Sybils may have already performed many malicious activities when being detected. In this work, we propose Ianus, a Sybil detection method that leverages account registration information. Ianus aims to catch Sybils immediately after they are registered. First, using a real- world registration dataset with labeled Sybils from WeChat (the largest online social network in China), we perform a measurement study to characterize the registration patterns of Sybils and benign users. We find that Sybils tend to have synchronized and abnormal registration patterns. Second, based on our measurement results, we model Sybil detection as a graph inference problem, which allows us to integrate heterogeneous features. In particular, we extract synchronization and anomaly based features for each pair of accounts, use the features to build a graph in which Sybils are densely connected with each other while a benign user is isolated or sparsely connected with other benign users and Sybils, and finally detect Sybils via analyzing the structure of the graph. We evaluate Ianus using real-world registration datasets of WeChat. Moreover, WeChat has deployed Ianus on a daily basis, i.e., WeChat uses Ianus to analyze newly registered accounts on each day and detect Sybils. Via manual verification by the WeChat security team, we find that Ianus can detect around 400K per million new registered accounts each day and achieve a precision of over 96% on average. In this paper, we propose a new solution Revery to search for exploitable states in paths diverging from crashing paths, and generate control-flow hijacking exploits for heap-based vulnerabilities. It adopts three novel techniques: (1) a layout-contributor digraph to characterize a vulnerability’s memory layout and its contributor instructions; (2) a layout-oriented fuzzing solution to explore diverging paths, which have similar memory layouts as the crashing paths, in order to search more exploitable states and generate correspond- ing diverging inputs; (3) a control-flow stitching solution to stitch crashing paths and diverging paths together, and synthesize EXP inputs able to trigger both vulnerabilities and exploitable states.