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Ask a(n)droid to tell you the odds: probabilistic security-by-contract for mobile devices

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Abstract

Security-by-contract is a paradigm proposed for the secure installation, usage, and monitoring of apps into mobile devices, with the aim of establishing, controlling, and, if necessary, enforcing security-critical behaviors. In this paper, we extend this paradigm with new functionalities allowing for a quantitative estimation of such behaviors, in order to reveal in real time the more and more challenging subtleties of new-generation malware and repackaged apps. The novel paradigm is based on formal means and techniques ranging from statistical analysis to probabilistic model checking. The framework, deployed in the Android environment, is evaluated by examining both its effectiveness with respect to a benchmark of real-world malware and its effect on the execution of genuine, secure apps.

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Notes

  1. https://www.welivesecurity.com/2017/10/13/doublelocker-innovative-android-malware/

References

  • Aldini A, Bernardo M (2007) A formal approach to the integrated analysis of security and QoS. Reliab. Eng. Syst. Saf. 92(11):1503–1520

    Article  Google Scholar 

  • Aldini A, Martinelli F, Saracino A, Sgandurra D (2015) Detection of repackaged mobile applications through a collaborative approach. Concurr. Comput.: Pract. Exp. 27(11):2818–2838

    Article  Google Scholar 

  • Aldini A, Seigneur JM, Lafuente C, Titi X, Guislain J (2017) Design and validation of a trust-based opportunity-enabled risk management system. Inf. Comput. Secur. 25(1):2–25. https://doi.org/10.1108/ICS-05-2016-0037

    Article  Google Scholar 

  • Aldini A, Bravetti M, Di Pierro A, Gorrieri R, Hankin C, Wiklicky H (2004) Two formal approaches for approximating noninterference properties. In: Foundations of security analysis and design II, LNCS, Springer, Berlin, vol 2946, pp 1–43

  • Aldini A, Di Pierro A (2004) A quantitative approach to noninterference for probabilistic systems. In: Proceedings of formal methods for security and time, ENTCS. vol 99, pp 155–182

  • Aldini A, Martinelli F, Saracino A, Sgandurra D (2013) A collaborative framework for generating probabilistic contracts. In: Proceedings of the 2013 IEEE international conference on collaboration technologies and systems, pp 139–143. 978-1-4763-6404-1/13

  • Backes M, Bugiel S, Derr E, Gerling S, Hammer C (2016) R-droid: leveraging android app analysis with static slice optimization. In: Proceedings of the 11th ACM on Asia conference on computer and communications security, ASIA CCS’16, pp 129–140. ACM, New York, NY, USA. https://doi.org/10.1145/2897845.2897927

  • Baier C, Katoen JP (2008) Principles of model checking. MIT Press, Cambridge

    MATH  Google Scholar 

  • Bergstra J, Ponse A, Smolka S (2001) Handbook of process algebra. Elsevier, Amsterdam

    MATH  Google Scholar 

  • Bielova N, Massacci F (2011) Predictability of enforcement. In: Proceedings of the international symposium on engineering secure software and systems, ESSoS’11, LNCS, Springer, Berlin. vol 6542, pp 73–86

  • BusinessOfApps: App statistic report. Tech. rep. (2016). Available at: http://www.businessofapps.com/data/app-statistics/

  • Cha SH (2007) Comprehensive survey on distance/similarity measures between probability density functions. Int. J. Math. Models Methods Appl. Sci. 1(4):300–307

    MathSciNet  Google Scholar 

  • Chen T, Forejt V, Kwiatkowska M, Parker D, Simaitis A (2013) PRISM-games: a model checker for stochastic multi-player games. In: Proceedings of the 19th international conference on tools and algorithms for the construction and analysis of systems (TACAS’13), LNCS. Springer, Berlin, vol 7795, pp 185–191

  • Costa G, Dragoni N, Lazouski A, Martinelli F, Massacci F, Matteucci I (2010) Extending Security-by-Contract with quantitative trust on mobile devices. In: Proceeding of the 4th international conference on complex, intelligent and software intensive systems, pp 872–877. IEEE CS

  • Delahaye B, Caillaud B, Legay A (2010) Probabilistic contracts: a compositional reasoning methodology for the design of stochastic systems. In: Procs. of 10th Int. Conf. on Application of Concurrency to System Design, ACSD’10, pp. 223–232. IEEE

  • Desharnais J, Gupta V, Jagadeesan R, Panangaden P (2004) Metrics for labelled Markov processes. Theoret Comput Sci 318:323–354

    Article  MathSciNet  Google Scholar 

  • Dini G, Martinelli F, Matteucci I, Petrocchi M, Saracino A, Sgandurra D (2016) Risk analysis of android applications: a user-centric solution. Future Gener Comput Syst. https://doi.org/10.1016/j.future.2016.05.035

    Article  Google Scholar 

  • Dini G, Martinelli F, Matteucci I, Petrocchi M, Saracino A, Sgandurra D (2012a) A multi-criteria-based evaluation of Android applications. In: Proceedings of the 4th international conference on trusted systems (INTRUST’12), LNCS. Springer, Berlin, pp. 67–82

  • Dini G, Martinelli F, Saracino A, Sgandurra D (2012b) Madam: A multi-level anomaly detector for android malware. In: Kotenko I, Skormin V (eds.) Computer Network Security, LNCS, Springer, Berlin. vol 7531, pp 240–253

  • Dragoni N, Martinelli F, Massacci F, Mori P, Schaefer C, Walter T, Vetillard E (2008) Security-by-contract (SxC) for software and services of mobile systems. In: Di Nitto E, Sassen AM, Traverso P, Zwegers A (eds) At your service—service-oriented computing from an EU Perspective. MIT Press, Cambridge, pp 429–456

    Google Scholar 

  • Easwaran A, Kannan S, Lee I (2005) Optimal control of software ensuring safety and functionality. Tech. Rep. MS-CIS-05-20, University of Pennsylvania

  • Enck W, Gilbert P, Chun BG, Cox LP, Jung J, McDaniel P, Sheth AN (2014) TaintDroid: an information flow tracking system for real-time privacy monitoring on smartphones. Commun ACM 57(3):99–106. https://doi.org/10.1145/2494522

    Article  Google Scholar 

  • Felt AP, Ha E, Egelman S, Haney A, Chin E, Wagner D (2012) Android permissions: user attention, comprehension, and behavior. In: Symposium on usable privacy and security, SOUPS ’12, Washington, DC, USA - July 11–13, 2012, p 3

  • Funk C, Garnaeva M (2013) Kaspersky security bullettin 2013. Tech. rep. http://media.kaspersky.com/pdf/KSB_2013_EN.pdf

  • Gascon H, Yamaguchi F, Arp D, Rieck K (2013) Structural detection of Android malware using embedded call graphs. In: Proceedings of the 2013 ACM workshop on artificial intelligence and security, AISec’13, pp 45–54. ACM. https://doi.org/10.1145/2517312.2517315

  • Grinstead C, Snell J (2012) Introduction to probability. American Mathematical Society, Providence

    MATH  Google Scholar 

  • Hoang X, Hu J (2004) An efficient hidden Markov model training scheme for anomaly intrusion detection of server applications based on system calls. In: Proceedings of 12th IEEE international conference on networks, ICON’04, vol 2, pp 470–474. IEEE

  • Kosoresow A, Hofmeyer S (1997) Intrusion detection via system call traces. Software 14(5):35–42

    Article  Google Scholar 

  • Kwiatkowska M, Norman G, Parker D (2011) PRISM 4.0: verification of probabilistic real-time systems. In: Gopalakrishnan G, Qadeer S (eds.) Proceedings of the 23rd international conference on computer aided verification (CAV’11), LNCS, vol 6806, pp 585–591. Springer, Berlin

  • Larsen R, Marx M (2011) An introduction to mathematical statistics and its applications. Pearson, London

    MATH  Google Scholar 

  • Maggi F, Matteucci M, Zanero S (2010) Detecting intrusions through system call sequence and argument analysis. IEEE Trans Dependable Secur Comput 7(4):381–395

    Article  Google Scholar 

  • Marra AL, Martinelli F, Saracino A, Aldini A (2016) On probabilistic application compliance. In: 2016 IEEE Conference Trustcom/BigDataSE/ISPA, Tianjin, China, pp 1848–1855. https://doi.org/10.1109/TrustCom.2016.0283

  • Martinelli F, Matteucci I (2007) Through modeling to synthesis of security automata. In: ENTCS 179

  • Martinelli F, Mercaldo F, Saracino A, Visaggio CA (2016) I find your behavior disturbing: Static and dynamic app behavioral analysis for detection of android malware. In: 2016 14th Annual conference on privacy, security and trust (PST), pp 129–136. https://doi.org/10.1109/PST.2016.7906947

  • Martinelli F, Morisset C (2012) Quantitative access control with partially-observable Markov decision processes. In: Proceedings of 2nd ACM conference on data and application security and privacy, CODASPY’12, pp 169–180. ACM, Cambridge

  • Ponemon Institute: The state of mobile application insecurity. Tech. rep. (2015)

  • Russell S, Norvig P (2010) Artificial intelligence: a modern approach. Prentice Hall, Cambridge

    MATH  Google Scholar 

  • Saracino A, Sgandurra D, Dini G, Martinelli F (2016) MADAM: effective and efficient behavior-based android malware detection and prevention. IEEE Trans Depend Secure Comput. https://doi.org/10.1109/TDSC.2016.2536605

    Article  Google Scholar 

  • Saracino A, Martinelli F, Alboreto G, Dini G (2016) Data-sluice: fine-grained traffic control for Android application. In: IEEE symposium on computers and communication, ISCC’16, pp 702–709. https://doi.org/10.1109/ISCC.2016.7543819

  • Suarez-Tangil G, Tapiador J, Lombardi F, Di Pietro R (2014) Thwarting obfuscated malware via differential fault analysis. Computer 47(6):24–31. https://doi.org/10.1109/MC.2014.169

    Article  Google Scholar 

  • Wang R, Azab AM, Enck W, Li N, Ning P, Chen X, Shen W, Cheng Y (2017) SPOKE: scalable knowledge collection and attack surface analysis of access control policy for security enhanced Android. In: Proceedings of the 2017 ACM on Asia conference on computer and communications security, ASIA CCS ’17, pp 612–624. ACM, New York, NY, USA. https://doi.org/10.1145/3052973.3052991

  • Xposedbridge development tutorial (2012). https://github.com/rovo89/XposedBridge/wiki/Development-tutorial

  • Zhang M, Duan Y, Yin H, Zhao Z (2014) Semantics-aware Android malware classification using weighted contextual API dependency graphs. In: Proceedingss of the 2014 ACM SIGSAC conference on computer and communications security, CCS’14, pp 1105–1116. ACM. https://doi.org/10.1145/2660267.2660359

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Acknowledgements

The activities of this paper have been partially Funded by H2020 Project C3ISP Grant Nos. 700264 and H2020 Project SPARTA Grant No. 830892.

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Correspondence to Andrea Saracino.

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Aldini, A., La Marra, A., Martinelli, F. et al. Ask a(n)droid to tell you the odds: probabilistic security-by-contract for mobile devices. Soft Comput 25, 2295–2314 (2021). https://doi.org/10.1007/s00500-020-05299-4

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