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 32p 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.

/pdf/taintdroid-an-information-flow-tracking-system-for-realtime-5d8smsr0iy.pdf

TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones

Today's smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use 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 provides realtime analysis by leveraging Android's virtualized execution environment. TaintDroid incurs only 14% performance overhead on a CPU-bound micro-benchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of potential misuse of users' private 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.

/pdf/taintdroid-an-information-flow-tracking-system-for-realtime-3fnz9lwdso.pdf

TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones

The popularity and adoption of smart phones has greatly stimulated the spread of mobile malware, especially on the popular platforms such as Android. In light of their rapid growth, there is a pressing need to develop effective solutions. However, our defense capability is largely constrained by the limited understanding of these emerging mobile malware and the lack of timely access to related samples. In this paper, we focus on the Android platform and aim to systematize or characterize existing Android malware. Particularly, with more than one year effort, we have managed to collect more than 1,200 malware samples that cover the majority of existing Android malware families, ranging from their debut in August 2010 to recent ones in October 2011. In addition, we systematically characterize them from various aspects, including their installation methods, activation mechanisms as well as the nature of carried malicious payloads. The characterization and a subsequent evolution-based study of representative families reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software. Based on the evaluation with four representative mobile security software, our experiments show that the best case detects 79.6% of them while the worst case detects only 20.2% in our dataset. These results clearly call for the need to better develop next-generation anti-mobile-malware solutions.

/pdf/dissecting-android-malware-characterization-and-evolution-2euhdmm33o.pdf

Dissecting Android Malware: Characterization and Evolution

Malicious applications pose a threat to the security of the Android platform. The growing amount and diversity of these applications render conventional defenses largely ineffective and thus Android smartphones often remain unprotected from novel malware. In this paper, we propose DREBIN, a lightweight method for detection of Android malware that enables identifying malicious applications directly on the smartphone. As the limited resources impede monitoring applications at run-time, DREBIN performs a broad static analysis, gathering as many features of an application as possible. These features are embedded in a joint vector space, such that typical patterns indicative for malware can be automatically identified and used for explaining the decisions of our method. In an evaluation with 123,453 applications and 5,560 malware samples DREBIN outperforms several related approaches and detects 94% of the malware with few false alarms, where the explanations provided for each detection reveal relevant properties of the detected malware. On five popular smartphones, the method requires 10 seconds for an analysis on average, rendering it suitable for checking downloaded applications directly on the device.

/pdf/drebin-effective-and-explainable-detection-of-android-4kuv3sjqel.pdf

DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket.

Today's smartphones are a ubiquitous source of private and confidential data. At the same time, smartphone users are plagued by carelessly programmed apps that leak important data by accident, and by malicious apps that exploit their given privileges to copy such data intentionally. While existing static taint-analysis approaches have the potential of detecting such data leaks ahead of time, all approaches for Android use a number of coarse-grain approximations that can yield high numbers of missed leaks and false alarms. In this work we thus present FlowDroid, a novel and highly precise static taint analysis for Android applications. A precise model of Android's lifecycle allows the analysis to properly handle callbacks invoked by the Android framework, while context, flow, field and object-sensitivity allows the analysis to reduce the number of false alarms. Novel on-demand algorithms help FlowDroid maintain high efficiency and precision at the same time. We also propose DroidBench, an open test suite for evaluating the effectiveness and accuracy of taint-analysis tools specifically for Android apps. As we show through a set of experiments using SecuriBench Micro, DroidBench, and a set of well-known Android test applications, FlowDroid finds a very high fraction of data leaks while keeping the rate of false positives low. On DroidBench, FlowDroid achieves 93% recall and 86% precision, greatly outperforming the commercial tools IBM AppScan Source and Fortify SCA. FlowDroid successfully finds leaks in a subset of 500 apps from Google Play and about 1,000 malware apps from the VirusShare project.

/pdf/flowdroid-precise-context-flow-field-object-sensitive-and-303w6s31hk.pdf

FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps

In this paper, we present a systematic study for the detection of malicious applications (or apps) on popular Android Markets. To this end, we first propose a permissionbased behavioral footprinting scheme to detect new samples of known Android malware families. Then we apply a heuristics-based filtering scheme to identify certain inherent behaviors of unknown malicious families. We implemented both schemes in a system called DroidRanger. The experiments with 204, 040 apps collected from five different Android Markets in May-June 2011 reveal 211 malicious ones: 32 from the official Android Market (0.02% infection rate) and 179 from alternative marketplaces (infection rates ranging from 0.20% to 0.47%). Among those malicious apps, our system also uncovered two zero-day malware (in 40 apps): one from the official Android Market and the other from alternative marketplaces. The results show that current marketplaces are functional and relatively healthy. However, there is also a clear need for a rigorous policing process, especially for non-regulated alternative marketplaces.

/pdf/hey-you-get-off-of-my-market-detecting-malicious-apps-in-2hdanyr5j8.pdf

Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets

Recent years have witnessed incredible popularity and adoption of smartphones and mobile devices, which is accompanied by large amount and wide variety of feature-rich smartphone applications. These smartphone applications (or apps), typically organized in different application marketplaces, can be conveniently browsed by mobile users and then simply clicked to install on a variety of mobile devices. In practice, besides the official marketplaces from platform vendors (e.g., Google and Apple), a number of third-party alternative marketplaces have also been created to host thousands of apps (e.g., to meet regional or localization needs). To maintain and foster a hygienic smartphone app ecosystem, there is a need for each third-party marketplace to offer quality apps to mobile users.In this paper, we perform a systematic study on six popular Android-based third-party marketplaces. Among them, we find a common "in-the-wild" practice of repackaging legitimate apps (from the official Android Market) and distributing repackaged ones via third-party marketplaces. To better understand the extent of such practice, we implement an app similarity measurement system called DroidMOSS that applies a fuzzy hashing technique to effectively localize and detect the changes from app-repackaging behavior. The experiments with DroidMOSS show a worrisome fact that 5% to 13% of apps hosted on these studied marketplaces are repackaged. Further manual investigation indicates that these repackaged apps are mainly used to replace existing in-app advertisements or embed new ones to "steal" or re-route ad revenues. We also identify a few cases with planted backdoors or malicious payloads among repackaged apps. The results call for the need of a rigorous vetting process for better regulation of third-party smartphone application marketplaces.

/pdf/detecting-repackaged-smartphone-applications-in-third-party-20w36niaif.pdf

Detecting repackaged smartphone applications in third-party android marketplaces

In recent years, there has been explosive growth in smartphone sales, which is accompanied with the availability of a huge number of smartphone applications (or simply apps). End users or consumers are attracted by the many interesting features offered by these devices and the associated apps. The developers of these apps are also benefited by the prospect of financial compensation, either by selling their apps directly or by embedding one of the many ad libraries available on smartphone platforms. In this paper, we focus on potential privacy and security risks posed by these embedded or in-app advertisement libraries (henceforth "ad libraries," for brevity). To this end, we study the popular Android platform and collect 100,000 apps from the official Android Market in March-May, 2011. Among these apps, we identify 100 representative in-app ad libraries (embedded in 52.1% of them) and further develop a system called AdRisk to systematically identify potential risks. In particular, we first decouple the embedded ad libraries from host apps and then apply our system to statically examine the ad libraries, ranging from whether they will upload privacy-sensitive information to remote (ad) servers or whether they will download untrusted code from remote servers. Our results show that most existing ad libraries collect private information: some of them may be used for legitimate targeting purposes (i.e., the user's location) while others are hard to justify by invasively collecting the information such as the user's call logs, phone number, browser bookmarks, or even the list of installed apps on the phone. Moreover, additional ones go a step further by making use of an unsafe mechanism to directly fetch and run code from the Internet, which immediately leads to serious security risks. Our investigation indicates the symbiotic relationship between embedded ad libraries and host apps is one main reason behind these exposed risks. These results clearly show the need for better regulating the way ad libraries are integrated in Android apps.

https://www.csc.ncsu.edu/faculty/jiang/pubs/WISEC12_ADRISK.pdf

Unsafe exposure analysis of mobile in-app advertisements

To build effective malware analysis techniques and to evaluate new detection tools, up-to-date datasets reflecting the current Android malware landscape are essential For such datasets to be maximally useful, they need to contain reliable and complete information on malware’s behaviors and techniques used in the malicious activities Such a dataset shall also provide a comprehensive coverage of a large number of types of malware The Android Malware Genome created circa 2011 has been the only well-labeled and widely studied dataset the research community had easy access to (As of 12/21/2015 the Genome authors have stopped supporting the dataset sharing due to resource limitation) But not only is it outdated and no longer represents the current Android malware landscape, it also does not provide as detailed information on malware’s behaviors as needed for research Thus it is urgent to create a high-quality dataset for Android malware While existing information sources such as VirusTotal are useful, to obtain the accurate and detailed information for malware behaviors, deep manual analysis is indispensable In this work we present our approach to preparing a large Android malware dataset for the research community We leverage existing anti-virus scan results and automation techniques in categorizing our large dataset (containing 24,650 malware app samples) into 135 varieties (based on malware behavioral semantics) which belong to 71 malware families For each variety, we select three samples as representatives, for a total of 405 malware samples, to conduct in-depth manual analysis Based on the manual analysis result we generate detailed descriptions of each malware variety’s behaviors and include them in our dataset We also report our observations on the current landscape of Android malware as depicted in the dataset Furthermore, we present detailed documentation of the process used in creating the dataset, including the guidelines for the manual analysis We make our Android malware dataset available to the research community

Deep Ground Truth Analysis of Current Android Malware

Mobile applications (or apps) are rapidly growing in number and variety. These apps provide useful features, but also bring certain privacy and security risks. For example, malicious authors may attach destructive payloads to legitimate apps to create so-called "piggybacked" apps and advertise them in various app markets to infect unsuspecting users. To detect them, existing approaches typically employ pair-wise comparison, which unfortunately has limited scalability. In this paper, we present a fast and scalable approach to detect these apps in existing Android markets. Based on the fact that the attached payload is not an integral part of a given app's primary functionality, we propose a module decoupling technique to partition an app's code into primary and non-primary modules. Also, noticing that piggybacked apps share the same primary modules as the original apps, we develop a feature fingerprint technique to extract various semantic features (from primary modules) and convert them into feature vectors. We then construct a metric space and propose a linearithmic search algorithm (with O(n log n) time complexity) to efficiently and scalably detect piggybacked apps. We have implemented a prototype and used it to study 84,767 apps collected from various Android markets in 2011. Our results show that the processing of these apps takes less than nine hours on a single machine. In addition, among these markets, piggybacked apps range from 0.97% to 2.7% (the official Android Market has 1%). Further investigation shows that they are mainly used to steal ad revenue from the original developers and implant malicious payloads (e.g., for remote bot control). These results demonstrate the effectiveness and scalability of our approach.

/pdf/fast-scalable-detection-of-piggybacked-mobile-applications-5cdwwhsrp7.pdf

Wu Zhou

Papers

Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets

Detecting repackaged smartphone applications in third-party android marketplaces

Unsafe exposure analysis of mobile in-app advertisements

Deep Ground Truth Analysis of Current Android Malware

Fast, scalable detection of "Piggybacked" mobile applications