The rise in popularity of the Android platform has resulted in an explosion of malware threats targeting it. As both Android malware and the operating system itself constantly evolve, it is very challenging to design robust malware mitigation techniques that can operate for long periods of time without the need for modifications or costly re-training. In this paper, we present MaMaDroid, an Android malware detection system that relies on app behavior. MaMaDroid builds a behavioral model, in the form of a Markov chain, from the sequence of abstracted API calls performed by an app, and uses it to extract features and perform classification. By abstracting calls to their packages or families, MaMaDroid maintains resilience to API changes and keeps the feature set size manageable. We evaluate its accuracy on a dataset of 8.5K benign and 35.5K malicious apps collected over a period of six years, showing that it not only effectively detects malware (with up to 99 F-measure), but also that the model built by the system keeps its detection capabilities for long periods of time (on average, 86 and 75 F-measure, respectively, one and two years after training). Finally, we compare against DroidAPIMiner, a state-of-the-art system that relies on the frequency of API calls performed by apps, showing that MaMaDroid significantly outperforms it.
@cite_37: Mobile malware threats have recently become a real concern. In this paper, we evaluate the state-of-the-art commercial mobile antimalware products for Android and test how resistant they are against various common obfuscation techniques (even with known malware). Such an evaluation is important for not only measuring the available defense against mobile malware threats but also proposing effective, next-generation solutions. We developed DroidChameleon, a systematic framework with various transformation techniques, and used it for our study. Our results on ten popular commercial anti-malware applications for Android are worrisome: none of these tools is resistant against common malware transformation techniques. Moreover, the transformations are simple in most cases and anti-malware tools make little effort to provide transformation-resilient detection. Finally, in the light of our results, we propose possible remedies for improving the current state of malware detection on mobile devices.
@cite_7: The prevalence of mobile platforms, the large market share of Android, plus the openness of the Android Market makes it a hot target for malware attacks. Once a malware sample has been identified, it is critical to quickly reveal its malicious intent and inner workings. In this paper we present DroidScope, an Android analysis platform that continues the tradition of virtualization-based malware analysis. Unlike current desktop malware analysis platforms, DroidScope reconstructs both the OS-level and Java-level semantics simultaneously and seamlessly. To facilitate custom analysis, DroidScope exports three tiered APIs that mirror the three levels of an Android device: hardware, OS and Dalvik Virtual Machine. On top of DroidScope, we further developed several analysis tools to collect detailed native and Dalvik instruction traces, profile API-level activity, and track information leakage through both the Java and native components using taint analysis. These tools have proven to be effective in analyzing real world malware samples and incur reasonably low performance overheads.
@cite_55: Smartphone usage has been continuously increasing in recent years. Moreover, smartphones are often used for privacy-sensitive tasks, becoming highly valuable targets for attackers. They are also quite different from PCs, so that PC-oriented solutions are not always applicable, or do not offer comprehensive security. We propose an alternative solution, where security checks are applied on remote security servers that host exact replicas of the phones in virtual environments. The servers are not subject to the same constraints, allowing us to apply multiple detection techniques simultaneously. We implemented a prototype of this security model for Android phones, and show that it is both practical and scalable: we generate no more than 2KiB s and 64B s of trace data for high-loads and idle operation respectively, and are able to support more than a hundred replicas running on a single server.
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@cite_10: Today's smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use their privacy-sensitive data. We address these shortcomings with TaintDroid, an efficient, systemwide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid provides real-time analysis by leveraging Android's virtualized execution environment. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of misappropriation of users' location and device identification information across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party applications for phone users and valuable input for smartphone security service firms seeking to identify misbehaving applications.
DroidScope @cite_7 and TaintDroid @cite_10 monitor run-time app behavior in a protected environment to perform dynamic taint analysis. DroidScope performs dynamic taint analysis at the machine code level, while TaintDroid monitors how third-party apps access or manipulate users' personal data, aiming to detect sensitive data leaving the system. However, as it is unrealistic to deploy dynamic analysis techniques directly on users' devices, due to the overhead they introduce, these are typically used offline @cite_37 @cite_40 @cite_1 . ParanoidAndroid @cite_55 employs a virtual clone of the smartphone, running in parallel in the cloud and replaying activities of the device -- however, even if minimal execution traces are actually sent to the cloud, this still takes a non-negligible toll on battery life.