Security and Privacy in Smartphones and Mobile Devices最新文献

{"title":"Sound and precise malware analysis for android via pushdown reachability and entry-point saturation","authors":"Shuying Liang, Andrew W. Keep, M. Might, Steven Lyde, Thomas Gilray, P. Aldous, David Van Horn","doi":"10.1145/2516760.2516769","DOIUrl":"https://doi.org/10.1145/2516760.2516769","url":null,"abstract":"Sound malware analysis of Android applications is challenging. First, object-oriented programs exhibit highly interprocedural, dynamically dispatched control structure. Second, the Android programming paradigm relies heavily on the asynchronous execution of multiple entry points. Existing analysis techniques focus more on the second challenge, while relying on traditional analytic techniques that suffer from inherent imprecision or unsoundness to solve the first.\u0000 We present Anadroid, a static malware analysis framework for Android apps. Anadroid exploits two techniques to soundly raise precision: (1) it uses a pushdown system to precisely model dynamically dispatched interprocedural and exception-driven control-flow; (2) it uses Entry-Point Saturation (EPS) to soundly approximate all possible interleavings of asynchronous entry points in Android applications. (It also integrates static taint-flow analysis and least permissions analysis to expand the class of malicious behaviors which it can catch.) Anadroid provides rich user interface support for human analysts which must ultimately rule on the \"maliciousness\" of a behavior.\u0000 To demonstrate the effectiveness of Anadroid's malware analysis, we had teams of analysts analyze a challenge suite of 52 Android applications released as part of the Automated Program Analysis for Cybersecurity (APAC) DARPA program. The first team analyzed the apps using a version of Anadroid that uses traditional (finite-state-machine-based) control-flow-analysis found in existing malware analysis tools; the second team analyzed the apps using a version of Anadroid that uses our enhanced pushdown-based control-flow-analysis. We measured machine analysis time, human analyst time, and their accuracy in flagging malicious applications. With pushdown analysis, we found statistically significant (p < 0.05) decreases in time: from 85 minutes per app to 35 minutes per app in human plus machine analysis time; and statistically significant (p < 0.05) increases in accuracy with the pushdown-driven analyzer: from 71% correct identification to 95% correct identification.","PeriodicalId":213305,"journal":{"name":"Security and Privacy in Smartphones and Mobile Devices","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114064965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The curse of 140 characters: evaluating the efficacy of SMS spam detection on android","authors":"Akshay Narayan, P. Saxena","doi":"10.1145/2516760.2516772","DOIUrl":"https://doi.org/10.1145/2516760.2516772","url":null,"abstract":"Many applications are available on Android market place for SMS spam filtering. In this paper, we conduct a detailed study of the methods used in spam filtering in these applications by reverse engineering them. Our study has three parts. First, we perform empirical tests to valuate accuracy and precision of these apps. Second, we test if we can use email spam classifiers on short text messages effectively. Empirical test results show that these email spam classifiers do not yield optimal accuracy (like they do on emails) when used with SMS data. Finally, in this work we develop a two-level stacked classifier for short text messages and demonstrate the improvement in accuracy over traditional Bayesian email spam filters. Our experimental results show that spam filtering precision and accuracy of nearly 98% (which is comparable with those of email classifiers) can be obtained using the stacked classifier we develop.","PeriodicalId":213305,"journal":{"name":"Security and Privacy in Smartphones and Mobile Devices","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125996742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Security composition in the real world: squaring the circle of mobile security with contemporary device economics","authors":"Jon A. Geater","doi":"10.1145/2516760.2516761","DOIUrl":"https://doi.org/10.1145/2516760.2516761","url":null,"abstract":"In a very short space of time consumer mobile devices have changed the way we live and work, resulting in huge amounts of sensitive data -- personal and corporate -- flowing through these tiny devices. As the value of data on these devices grows so do the threats they face, and the unique way the mobile industry works presents many challenges to achieving verifiable security while enabling an open ecosystem. Modern mobile devices are complex composed systems made up of multiple off-the-shelf components in hardware (SoC, GPU, memories), software (OS, drivers, applications) and firmware (boot stack). The devices have a relatively short life and are updated/replaced at a very fast pace, meaning that development, test and maintenance cycles are very short and major components frequently change from generation to generation. Achieving and maintaining whole system security in this scenario is extremely difficult. This keynote introduces some of the past and near future hardware assisted mobile security techniques and highlights some of the key areas of research needed to improve quality and confidence in the security of applications in these fast-evolving composed systems.","PeriodicalId":213305,"journal":{"name":"Security and Privacy in Smartphones and Mobile Devices","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134254177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A case of collusion: a study of the interface between ad libraries and their apps","authors":"Theodore Book, D. Wallach","doi":"10.1145/2516760.2516762","DOIUrl":"https://doi.org/10.1145/2516760.2516762","url":null,"abstract":"A growing concern with advertisement libraries on Android is their ability to exfiltrate personal information from their host applications. While previous work has looked at the libraries' abilities to extract private information from the system, advertising libraries also include APIs through which a host application can deliberately leak private information about the user. This study, considering a corpus of 114,000 apps, is the first to focus on those APIs. We reconstruct the APIs for 103 ad libraries used in the corpus, and study how the privacy leaking APIs from the top 20 ad libraries are used by the 64,000 applications in which they are included. Notably, we have found that app popularity correlates with privacy leakage; the marginal increase in advertising revenue, multiplied over a larger user base, seems to incentivize these app vendors to violate their users' privacy.","PeriodicalId":213305,"journal":{"name":"Security and Privacy in Smartphones and Mobile Devices","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133238548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"I've got 99 problems, but vibration ain't one: a survey of smartphone users' concerns","authors":"A. Felt, Serge Egelman, D. Wagner","doi":"10.1145/2381934.2381943","DOIUrl":"https://doi.org/10.1145/2381934.2381943","url":null,"abstract":"Smartphone operating systems warn users when third-party applications try to access sensitive functions or data. However, all of the major smartphone platforms warn users about different application actions. To our knowledge, their selection of warnings was not grounded in user research; past research on mobile privacy has focused exclusively on the risks pertained to sharing location. To expand the scope of smartphone security and privacy research, we surveyed 3,115 smartphone users about 99 risks associated with 54 smartphone privileges. We asked participants to rate how upset they would be if given risks occurred and used this data to rank risks by levels of user concern. We then asked 41 smartphone users to discuss the risks in their own words; their responses confirmed that people find the lowest-ranked risks merely annoying but might seek legal or financial retribution for the highest-ranked risks. In order to determine the relative frequency of risks, we also surveyed the 3,115 users about experiences with \"misbehaving\" applications. Our ranking and frequency data can be used to guide the selection of warnings on smartphone platforms.","PeriodicalId":213305,"journal":{"name":"Security and Privacy in Smartphones and Mobile Devices","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127966636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dr. Android and Mr. Hide: fine-grained permissions in android applications","authors":"Jinseong Jeon, Kristopher K. Micinski, J. A. Vaughan, Ari Fogel, N. Reddy, J. Foster, T. Millstein","doi":"10.1145/2381934.2381938","DOIUrl":"https://doi.org/10.1145/2381934.2381938","url":null,"abstract":"Google's Android platform includes a permission model that protects access to sensitive capabilities, such as Internet access, GPS use, and telephony. While permissions provide an important level of security, for many applications they allow broader access than actually required. In this paper, we introduce a novel framework that addresses this issue by adding finer-grained permissions to Android. Underlying our framework is a taxonomy of four major groups of Android permissions, each of which admits some common strategies for deriving sub-permissions. We used these strategies to investigate fine-grained versions of five of the most common Android permissions, including access to the Internet, user contacts, and system settings. We then developed a suite of tools that allow these fine-grained permissions to be inferred on existing apps; to be enforced by developers on their own apps; and to be retrofitted by users on existing apps. We evaluated our tools on a set of top apps from Google Play, and found that fine-grained permissions are applicable to a wide variety of apps and that they can be retrofitted to increase security of existing apps without affecting functionality.","PeriodicalId":213305,"journal":{"name":"Security and Privacy in Smartphones and Mobile Devices","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115783281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short paper: location privacy: user behavior in the field","authors":"D. Fisher, Leah Dorner, D. Wagner","doi":"10.1145/2381934.2381945","DOIUrl":"https://doi.org/10.1145/2381934.2381945","url":null,"abstract":"Current smartphone platforms provide ways for users to control access to information about their location. For instance, on the iPhone, when an application requests access to location information, the operating system asks the user whether to grant location access to this application. In this paper, we study how users are using these controls. Do iPhone users allow applications to access their location? Do their decisions differ from application to application? Can we predict how a user will respond for a particular application, given their past responses for other applications?\u0000 We gather data from iPhone users that sheds new light on these questions. Our results indicate that there are different classes of users: some deny all applications access to their location, some allow all applications access to their location, and some selectively permit a fraction of their applications to access their location. We also find that apps can be separated into different classes by what fraction of users trust the app with their location data. Finally, we investigate using machine learning techniques to predict users' location-sharing decisions; we find that we are sometimes able to predict the user's actual choice, though there is considerable room for improvement. If it is possible to improve the accuracy rate further, this information could be used to relieve users of the cognitive burden of individually assigning location permissions for each application, allowing users to focus their attention on more critical matters.","PeriodicalId":213305,"journal":{"name":"Security and Privacy in Smartphones and Mobile Devices","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130891095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing attack surfaces for intra-application communication in android","authors":"David Kantola, Erika Chin, Warren He, D. Wagner","doi":"10.1145/2381934.2381948","DOIUrl":"https://doi.org/10.1145/2381934.2381948","url":null,"abstract":"The complexity of Android's message-passing system has led to numerous vulnerabilities in third-party applications. Many of these vulnerabilities are a result of developers confusing inter-application and intra-application communication mechanisms. Consequently, we propose modifications to the Android platform to detect and protect inter-application messages that should have been intra-application messages. Our approach automatically reduces attack surfaces in legacy applications. We describe our implementation for these changes and evaluate it based on the attack surface reduction and the extent to which our changes break compatibility with a large set of popular applications. We fix 100% of intra-application vulnerabilities found in our previous work, which represents 31.4% of the total security flaws found in that work. Furthermore, we find that 99.4% and 93.0% of Android applications are compatible with our sending and receiving changes, respectively.","PeriodicalId":213305,"journal":{"name":"Security and Privacy in Smartphones and Mobile Devices","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126105020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short paper: smartphones: not smart enough?","authors":"I. Fischer, C. Kuo, Ling Huang, Mario Frank","doi":"10.1145/2381934.2381941","DOIUrl":"https://doi.org/10.1145/2381934.2381941","url":null,"abstract":"Today's mobile devices are packed with sensors that are capable of gathering rich contextual information, such as location, wireless device signatures, ambient noise, and photographs. This paper exhorts the security community to re-design authentication mechanisms for users on mobile devices. Instead of relying on one simplistic, worst-case threat model, we should use contextual information to develop more nuanced models that assess the risk level of the user's current environment. This would allow us to decrease or eliminate the level of user interaction required to authenticate in some situations, improving usability without any effective impact on security. Ideally, authentication mechanisms will scale up or down to match users' own mental threat models of their environments. We sketch out several scenarios demonstrating how contextual information can be used to assess risks and adapt authentication mechanisms. This is a research-rich area, and we outline future research directions for developing and evaluating dynamic security mechanisms using contextual information.","PeriodicalId":213305,"journal":{"name":"Security and Privacy in Smartphones and Mobile Devices","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121000507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short paper: enhancing mobile application permissions with runtime feedback and constraints","authors":"Jaeyeon Jung, Seungyeop Han, D. Wetherall","doi":"10.1145/2381934.2381944","DOIUrl":"https://doi.org/10.1145/2381934.2381944","url":null,"abstract":"We report on a field study that uses a combination of OS measurements and qualitative interviews to highlight gaps between user expectations with respect to privacy and the result of using the existing permissions architecture to install mobile apps. Most of our participants expected advertising and analytics behavior, yet they were often surprised by applications' data collection in the background and the level of data sharing with third parties that actually occurred. Given participant feedback, we propose platform support to reduce this \"expectation gap\" with transparency of data usage and constrained permissions.","PeriodicalId":213305,"journal":{"name":"Security and Privacy in Smartphones and Mobile Devices","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123359015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}