No abstract

In this work we explore cyberbullying and other toxic behavior in team competition online games. Using a dataset of over 10 million player reports on 1.46 million toxic players along with corresponding crowdsourced decisions, we test several hypotheses drawn from theories explaining toxic behavior. Besides providing large-scale, empirical based understanding of toxic behavior, our work can be used as a basis for building systems to detect, prevent, and counter-act toxic behavior.

Today’s smartphone operating systems frequently fail to provide users with visibility into how third-party applications collect and share their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid enables realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 32% performance overhead on a CPU-bound microbenchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, in our 2010 study we found 20 applications potentially misused users’ private information; so did a similar fraction of the tested applications in our 2012 study. Monitoring the flow of privacy-sensitive data with TaintDroid provides valuable input for smartphone users and security service firms seeking to identify misbehaving applications.

Current app stores distribute some malware to unsuspecting users, even though the app approval process may be costly and timeconsuming. High-integrity app stores must provide stronger guarantees that their apps are not malicious. We propose a verification model for use in such app stores to guarantee that the apps are free of malicious information flows. In our model, the software vendor and the app store auditor collaborate -each does tasks that are easy for her/him, reducing overall verification cost. The software vendor provides a behavioral specification of information flow (at a finer granularity than used by current app stores) and source code annotated with information-flow type qualifiers. A flow-sensitive, context-sensitive information-flow type system checks the information flow type qualifiers in the source code and proves that only information flows in the specification can occur at run time. The app store auditor uses the vendor-provided source code to manually verify declassifications.We have implemented the information-flow type system for Android apps written in Java, and we evaluated both its effectiveness at detecting information-flow violations and its usability in practice. In an adversarial Red Team evaluation, we analyzed 72 apps (576,000 LOC) for malware. The 57 Trojans among these had been written specifically to defeat a malware analysis such as ours. Nonetheless, our information-flow type system was effective: it detected 96% of malware whose malicious behavior was related to information flow and 82% of all malware. In addition to the adversarial evaluation, we evaluated the practicality of using the collaborative model. The programmer annotation burden is low: 6 annotations per 100 LOC. Every sound analysis requires a human to review potential false alarms, and in our experiments, this took 30 minutes per 1,000 LOC for an auditor unfamiliar with the app.