Advertisement

Dynamic malware detection and phylogeny analysis using process mining

  • Mario Luca Bernardi
  • Marta CimitileEmail author
  • Damiano Distante
  • Fabio Martinelli
  • Francesco Mercaldo
Regular Contribution

Abstract

In the last years, mobile phones have become essential communication and productivity tools used daily to access business services and exchange sensitive data. Consequently, they also have become one of the biggest targets of malware attacks. New malware is created everyday, most of which is generated as variants of existing malware by reusing its malicious code. This paper proposes an approach for malware detection and phylogeny studying based on dynamic analysis using process mining. The approach exploits process mining techniques to identify relationships and recurring execution patterns in the system call traces gathered from a mobile application in order to characterize its behavior. The recovered characterization is expressed in terms of a set of declarative constraints between system calls and represents a sort of run-time fingerprint of the application. The comparison between the so defined fingerprint of a given application with those of known malware is used to verify: (1) if the application is malware or trusted, (2) in case of malware, which family it belongs to, and (3) how it differs from other known variants of the same malware family. An empirical study conducted on a dataset of 1200 trusted and malicious applications across ten malware families has shown that the approach exhibits a very good discrimination ability that can be exploited for malware detection and malware evolution studying. Moreover, the study has also shown that the approach is robust to code obfuscation techniques increasingly being used by nowadays malware.

Keywords

Malware detection Malware evolution Malware phylogeny Security Process mining Linear temporal logic Declare 

Notes

Acknowledgements

This work has been partially supported by H2020 EU-funded projects NeCS and C3ISP and EIT-Digital Project HII.

References

  1. 1.
    Androguard. https://code.google.com/p/androguard/, last visit 24 November 2014
  2. 2.
    Anderson, B., Storlie, C., Lane, T.: Improving malware classification: bridging the static/dynamic gap. In: Proceedings of the 5th ACM Workshop on Security and Artificial Intelligence, AISec ’12, pp. 3–14, New York, NY, USA. ACM (2012)Google Scholar
  3. 3.
    Arora, A., Garg, S., Peddoju, S.K.: Malware detection using network traffic analysis in android based mobile devices. In: 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies (NGMAST), pp. 66–71 (Sept 2014)Google Scholar
  4. 4.
    Arp, D., Spreitzenbarth, M., Huebner, M., Gascon, H., Rieck, K.: DREBIN: efficient and explainable detection of android malware in your pocket. In: Proceedings of 21th Annual Network and Distributed System Security Symposium (NDSS) (2014)Google Scholar
  5. 5.
    Battista, P., Mercaldo, F., Nardone, V., Santone, A., Visaggio, C.A.: Identification of android malware families with model checking. In: International Conference on Information Systems Security and Privacy. SCITEPRESS (2016)Google Scholar
  6. 6.
    Bernardi, M.L., Cimitile, M., Di Francescomarino, C., Maggi, F.M.: Do activity lifecycles affect the validity of a business rule in a business process? Inf. Syst. 62, 42–59 (2016)CrossRefGoogle Scholar
  7. 7.
    Bernardi, M.L., Cimitile, M., Di Lucca, G.A., Maggi, F.M.: Using declarative workflow languages to develop process-centric web applications. In: 16th IEEE International Enterprise Distributed Object Computing Conference Workshops, EDOC Workshops, Beijing, China, September 10–14, 2012, pp. 56–65 (2012)Google Scholar
  8. 8.
    Bernardi, M.L., Cimitile, M., Mercaldo, F., Distante, D.: A constraint-driven approach for dynamic malware detection. In: 14th IEEE Annual Conference on Privacy Security and Trust (2016)Google Scholar
  9. 9.
    Bose, R.P., Maggi, F.M., Aalst, W.M.P.: Enhancing Declare Maps Based on Event Correlations, chapter Business Process Management: 11th International Conference, BPM 2013, Beijing, China, August 26–30, 2013. Proceedings, pp. 97–112. Springer, Berlin (2013)Google Scholar
  10. 10.
    Burattin, A., Cimitile, M., Maggi, F.M., Sperduti, A.: Online discovery of declarative process models from event streams. IEEE Trans. Serv. Comput. 8(6), 833–846 (2015)CrossRefGoogle Scholar
  11. 11.
    Canfora, G., Mercaldo, F., Visaggio, C.A.: A classifier of malicious android applications. In: 2013 Eighth International Conference on Availability, Reliability and Security (ARES), pp. 607–614 (Sept 2013)Google Scholar
  12. 12.
    Canfora, G., Di Sorbo, A., Mercaldo, F., Visaggio, C.A.: Obfuscation techniques against signature-based detection: a case study. In: 2015 Mobile Systems Technologies Workshop (MST), pp. 21–26. IEEE (2015)Google Scholar
  13. 13.
    Canfora, G., Medvet, E., Mercaldo, F., Visaggio, C.A.: Availability, Reliability, and Security in Information Systems: IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2014 and 4th International Workshop on Security and Cognitive Informatics for Homeland Defense, SeCIHD 2014, Fribourg, Switzerland, September 8–12, 2014. Proceedings, chapter Detection of Malicious Web Pages Using System Calls Sequences, pp. 226–238. Springer, Cham (2014)Google Scholar
  14. 14.
    Canfora, G., Medvet, E., Mercaldo, F., Visaggio, C.A.: Detecting android malware using sequences of system calls. In: Proceedings of the 3rd International Workshop on Software Development Lifecycle for Mobile, DeMobile 2015, pp. 13–20, New York, NY, USA, 2015. ACM (2015)Google Scholar
  15. 15.
    Carrera, E., Erdélyi, G.: Digital genome mapping—advanced binary malware analysis. In: Virus Bulletin Conference, Vol. 11 (2004)Google Scholar
  16. 16.
    Chin, E., Felt, A.P., Greenwood, K., Wagner, D.: Analyzing inter-application communication in android. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, MobiSys ’11, pp. 239–252, New York, NY, USA, 2011. ACM (2011)Google Scholar
  17. 17.
    Enck, W., Octeau, D., McDaniel, P., Chaudhuri, S.: A study of android application security. In: Proceedings of the 20th USENIX Conference on Security, SEC’11, pp. 21–21, Berkeley, CA, USA, 2011. USENIX Association (2011)Google Scholar
  18. 18.
    Gartner Report of February 2017. http://www.gartner.com/newsroom/id/3609817 (2017)
  19. 19.
    Hayes, M., Walenstein, A., Lakhotia, A.: Evaluation of malware phylogeny modelling systems using automated variant generation. J. Comput. Virol. 5(4), 335–343 (2008)CrossRefGoogle Scholar
  20. 20.
    Holmes, G., Donkin, A., Witten, I.H.: Weka: A machine learning workbench. In: Proceedings of the Second Australia and New Zealand Conference on Intelligent Information Systems, pp. 357–361. Citeseer (1994)Google Scholar
  21. 21.
    Isohara, T., Takemori, K., Kubota, A.: Kernel-based behavior analysis for android malware detection. In: Proceedings of the 2011 Seventh International Conference on Computational Intelligence and Security, CIS ’11, pp. 1011–1015, Washington, DC, USA, 2011. IEEE Computer Society (2011)Google Scholar
  22. 22.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)CrossRefGoogle Scholar
  23. 23.
    Jang, J., Brumley, D., Venkataraman, S.: BitShred: feature hashing malware for scalable triage and semantic analysis. In: Proceedings of the 18th ACM Conference on Computer and Communications Security, CCS ’11, pp. 309–320, New York, NY, USA, 2011. ACM (2011)Google Scholar
  24. 24.
    Jeong, Y., Lee, H., Cho, S., Han, S., Park, M.: A kernel-based monitoring approach for analyzing malicious behavior on android. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC ’14, pp. 1737–1738, New York, NY, USA, 2014. ACM (2014)Google Scholar
  25. 25.
    Jiang, X., Zhou, Y.: Android Malware. Springer, New York (2013)CrossRefGoogle Scholar
  26. 26.
    Karim, M.E., Walenstein, A., Lakhotia, A., Parida, L.: Malware phylogeny generation using permutations of code. J. Comput. Virol. 1(1–2), 13–23 (2005)CrossRefGoogle Scholar
  27. 27.
    Khoo, W.M., Lió, P.: Unity in diversity: phylogenetic-inspired techniques for reverse engineering and detection of malware families. In: 2011 First SysSec Workshop (SysSec), pp. 3–10. IEEE (2011)Google Scholar
  28. 28.
    Ma, J., Dunagan, J., Wang, H.J., Savage, S., Voelker, G.M.: Finding diversity in remote code injection exploits. In: Proceedings of the 6th ACM SIGCOMM Conference on Internet Measurement, IMC ’06, pp. 53–64, New York, NY, USA, 2006. ACM (2006)Google Scholar
  29. 29.
    Mobile Threat Report. https://www.f-secure.com/documents/996508/1030743/Threat_Report_H1_2014.pdf, last visit 26 February 2016
  30. 30.
    Mario, F.M., Bernardi, L., Cimitile, M.: Process mining meets malware evolution: a study of the behavior of malicious code. In: 2015 Fourth International Symposium on Computing and Networking (CANDAR) (Dec 2016)Google Scholar
  31. 31.
    Mercaldo, F., Nardone, V., Santone, A., Visaggio, C.A.: Download malware? No, thanks. How formal methods can block update attacks. In: Proceedings of the 4th FME Workshop on Formal Methods in Software Engineering, pp. 22–28. ACM (2016)Google Scholar
  32. 32.
    Mercaldo, F., Nardone, V., Santone, A., Visaggio, C.A.: Ransomware steals your phone. Formal methods rescue it. In: International Conference on Formal Techniques for Distributed Objects, Components, and Systems, pp. 212–221. Springer (2016)Google Scholar
  33. 33.
    Oberheide, J., Mille, C.: Dissecting the android bouncer. In: SummerCon (2012)Google Scholar
  34. 34.
    Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: Declare: full support for loosely-structured processes. EDOC 2007, 287–300 (2007)Google Scholar
  35. 35.
    Picinbono, B.: On deflection as a performance criterion in detection. IEEE Trans. Aerosp. Electron. Syst. 31(3), 1072–1081 (1995)CrossRefGoogle Scholar
  36. 36.
    Rastogi, V., Chen, Y., Jiang, X.: Catch me if you can: evaluating android anti-malware against transformation attacks. IEEE Trans. Inf. Forensics Secur. 9(1), 99–108 (2014)CrossRefGoogle Scholar
  37. 37.
    Rastogi, V., Chen, Y., Jiang, X.: DroidChameleon: evaluating android anti-malware against transformation attacks. In: Proceedings of the 8th ACM SIGSAC Symposium on Information, Computer and Communications Security, ASIA CCS ’13, pp. 329–334, New York, NY, USA, 2013. ACM (2013)Google Scholar
  38. 38.
    Reina, A., Fattori, A., Cavallaro, L.: A system call-centric analysis and stimulation technique to automatically reconstruct android malware behaviors. In: Proceedings of EuroSec (2013)Google Scholar
  39. 39.
    Sahs, J., Khan, L.: A machine learning approach to android malware detection. In: Proceedings of the European Intelligence and Security Informatics Conference (2012)Google Scholar
  40. 40.
    Schmidt, A.-D., Schmidt, H.-G., Clausen, J., Yuksel, K.A., Kiraz, O., Camtepe, A., Albayrak, S.: Enhancing security of linux-based android devices. In: Proceedings of 15th International Linux Kongress (2008)Google Scholar
  41. 41.
    Spreitzenbarth, M., Freiling, F., Echtler, F., Schreck, T., Hoffmann, J.: Mobile-sandbox: having a deeper look into android applications. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC ’13, pp. 1808–1815, New York, NY, USA, 2013. ACM (2013)Google Scholar
  42. 42.
    Tchakounté, F., Dayang, P.: System calls analysis of malwares on android. Int. J. Sci. Tecnol. (IJST) 2(9), 669–674 (2013)Google Scholar
  43. 43.
    van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin (2011)CrossRefzbMATHGoogle Scholar
  44. 44.
    van Dongen, B.F., de Medeiros,A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The prom framework: a new era in process mining tool support. In: Proceedings of the 26th International Conference on Applications and Theory of Petri Nets, ICATPN’05, pp. 444–454, Berlin, Heidelberg, 2005. Springer (2005)Google Scholar
  45. 45.
    Virustotal. https://www.virustotal.com/, last visit 1 March 2016
  46. 46.
    Walenstein, A., Lakhotia, A.: A transformation-based model of malware derivation. In: 2012 7th International Conference on Malicious and Unwanted Software (MALWARE), pp. 17–25 (Oct 2012)Google Scholar
  47. 47.
    Wang, X., Jhi, Y.-C., Zhu, S., Liu, P.: Detecting software theft via system call based birthmarks. In: Proceedings of the 2009 Annual Computer Security Applications Conference, ACSAC ’09, pp. 149–158, Washington, DC, USA, 2009. IEEE Computer Society (2009)Google Scholar
  48. 48.
    Wei, T.-E., Mao, C.-H., Jeng, A.B., Lee, H.-M., Wang, H.-T., Wu, D.-J.: Android malware detection via a latent network behavior analysis. In: Proceedings of the 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications, TRUSTCOM ’12, pp. 1251–1258, Washington, DC, USA, 2012. IEEE Computer Society (2012)Google Scholar
  49. 49.
    Xiao, X., Zhang, S., Mercaldo, F., Hu, G., Sangaiah, A.K.: Android malware detection based on system call sequences and LSTM. Multimedia Tools and Applications (Sept 2017)Google Scholar
  50. 50.
    Yan, L.K., Yin, H.: DroidScope: Seamlessly reconstructing the OS and Dalvik semantic views for dynamic android malware analysis. In: Proceedings of the 21st USENIX Conference on Security Symposium, Security’12, pp. 29–29, Berkeley, CA, USA, 2012. USENIX Association (2012)Google Scholar
  51. 51.
    Zheng, M., Lee, P.P.C., Lui, J.C.S.: ADAM: an automatic and extensible platform to stress test android anti-virus systems. In: International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment. pp. 82–101. Springer (2012)Google Scholar
  52. 52.
    Zheng, M., Sun, M., Lui, J.C.S.: Droid analytics: a signature based analytic system to collect, extract, analyze and associate android malware. In: Proceedings of the 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TRUSTCOM ’13, pp. 163–171, Washington, DC, USA, 2013. IEEE Computer Society (2013)Google Scholar
  53. 53.
    Zhou, W., Zhou, Y., Jiang, X., Ning, P.: Detecting repackaged smartphone applications in third-party android marketplaces. In: Proceedings of the Second ACM Conference on Data and Application Security and Privacy, CODASPY ’12, pp. 317–326, New York, NY, USA, 2012. ACM (2012)Google Scholar
  54. 54.
    Zhou, Y., Jiang, X.: Dissecting android malware: characterization and evolution. In: Proceedings of the 2012 IEEE Symposium on Security and Privacy, SP ’12, pp. 95–109, Washington, DC, USA, 2012. IEEE Computer Society (2012)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Mario Luca Bernardi
    • 1
  • Marta Cimitile
    • 2
    Email author
  • Damiano Distante
    • 2
  • Fabio Martinelli
    • 3
  • Francesco Mercaldo
    • 3
  1. 1.Giustino Fortunato UniversityBeneventoItaly
  2. 2.University of Rome Unitelma SapienzaRomeItaly
  3. 3.Institute for Informatics and TelematicsCNRPisaItaly

Personalised recommendations