Acquiring and analyzing app metrics for effective mobile malware detection

IWSPA 2016 - Proceedings of the 2016 ACM International Workshop on Security and Privacy Analytics, co-located with CODASPY 2016

Volumn , Issue , 2016, Pages 50-57

  Canfora, Gerardo ^a   Medvet, Eric ^b   Mercaldo, Francesco ^a   Visaggio, Corrado Aaron ^a  

^a UNIVERSITY OF SANNIO   (Italy)

^b UNIVERSITY OF TRIESTE   (Italy)

Author keywords

Android; Machine Learning; Malware

Indexed keywords

ANDROID (OPERATING SYSTEM); ARTIFICIAL INTELLIGENCE; COMPUTER CRIME; LEARNING SYSTEMS;

ANDROID; ANDROID MALWARE; DEVICE RESOURCES; DISCRIMINATIVE POWER; MALICIOUS BEHAVIOR; MALWARE DETECTION; MOBILE MALWARE; SIGNATURE BASED DETECTIONS;

MALWARE;

EID: 84966660819     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2875475.2875481     Document Type: Conference Paper

References (32)

1. A. Apvrille and T. Strazzere. Reducing the window of opportunity for android malware gotta catch'em all. Journal in Computer Virology, 8(1-2):61-71, 2012.
2. A. Arora, S. Garg, and S. K. Peddoju. Malware detection using network trafic analysis in android based mobile devices. In Next Generation Mobile Apps, Services and Technologies (NGMAST), 2014 Eighth International Conference on, pages 66-71. IEEE, 2014.
3. D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, and K. Rieck. Drebin: Efficient and explainable detection of android malware in your pocket. In Proc. of 17th Network and Distributed System Security Symposium, NDSS, volume 14.
4. T. Bläsing, L. Batyuk, A.-D. Schmidt, S. A. Camtepe, and S. Albayrak. An android application sandbox system for suspicious software detection. In Malicious and unwanted software (MALWARE), 2010 5th international conference on, pages 55-62. IEEE, 2010.
5. A. Boukhtouta, N.-E. Lakhdari, and M. Debbabi. Inferring malware family through application protocol sequences signature. In New Technologies, Mobility and Security (NTMS), 2014 6th International Conference on, pages 1-5. IEEE, 2014.
6. L. Breiman. Random forests. Machine learning, 45(1):5-32, 2001.
7. G. Canfora, E. Medvet, F. Mercaldo, and C. A. Visaggio. Detecting android malware using sequences of system calls. In Proceedings of the 3rd International Workshop on Software Development Lifecycle for Mobile, pages 13-20. ACM, 2015.
8. G. Canfora, F. Mercaldo, and C. A. Visaggio. A classifier of malicious android applications. In Availability, Reliability and Security (ARES), 2013 Eighth International Conference on, pages 607-614. IEEE, 2013.
9. L. Corral, A. B. Georgiev, A. Janes, and S. Koer. Energy-aware performance evaluation of android custom kernels. In Proceedings of the Fourth International Workshop on Green and Sustainable Software, pages 1-7. IEEE Press, 2015.
10. A. Cuzzocrea, E. Medvet, E. Mumolo, and R. Cecolin. Detecting anomalies in embedded computing systems via a novel hmm-based machine learning approach. In Hybrid Artificial Intelligent Systems, pages 405-415. Springer, 2015.
11. T. Deryckere, W. Joseph, L. Martens, L. De Marez, K. De Moor, and K. Berte. A software tool to relate technical performance to user experience in a mobile context. In World of Wireless, Mobile and Multimedia Networks, 2008. WoWMoM 2008. 2008 International Symposium on a, pages 1-6. IEEE, 2008.
12. B. Dixon, Y. Jiang, A. Jaiantilal, and S. Mishra. Location based power analysis to detect malicious code in smartphones. In Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices, pages 27-32. ACM, 2011.
13. W. Enck, P. Gilbert, S. Han, V. Tendulkar, B.-G. Chun, L. P. Cox, J. Jung, P. McDaniel, and A. N. Sheth. Taintdroid: an information-ow tracking system for realtime privacy monitoring on smartphones. ACM Transactions on Computer Systems (TOCS), 32(2):5, 2014.
14. S. Hao, D. Li, W. G. Halfond, and R. Govindan. Estimating android applications' cpu energy usage via bytecode profiling. In Proceedings of the First International Workshop on Green and Sustainable Software, pages 1-7. IEEE Press, 2012.
15. H. Kim, J. Smith, and K. G. Shin. Detecting energy-greedy anomalies and mobile malware variants. In Proceedings of the 6th international conference on Mobile systems, applications, and services, pages 239-252. ACM, 2008.
16. J. Kwon, J. Jeong, J. Lee, and H. Lee. Droidgraph: discovering android malware by analyzing semantic behavior. In Communications and Network Security (CNS), 2014 IEEE Conference on, pages 498-499. IEEE, 2014.
17. L. Kwong and Y. H. Yin. Droidscope: seamlessly reconstructing the os and dalvik semantic views for dynamic android malware analysis. 2012.
18. J. Li, L. Zhai, X. Zhang, and D. Quan. Research of android malware detection based on network trafic monitoring. In Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on, pages 1739-1744. IEEE, 2014.
19. Q. Li and X. Li. Android malware detection based on static analysis of characteristic tree. In Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2015 International Conference on, pages 84-91. IEEE, 2015.
20. L. Liu, G. Yan, X. Zhang, and S. Chen. Virusmeter: Preventing your cellphone from spies. In Recent Advances in Intrusion Detection, pages 244-264. Springer, 2009.
21. J. Oberheide, E. Cooke, and F. Jahanian. Cloudav: N-version antivirus in the network cloud. In USENIX Security Symposium, pages 91-106, 2008.
22. J. Oberheide and C. Miller. Dissecting the android bouncer. SummerCon2012, New York, 2012.
23. B. Rahbarinia, R. Perdisci, and M. Antonakakis. Segugio: Efficient behavior-based tracking of malware-control domains in large isp networks. In Dependable Systems and Networks (DSN), 2015 45th Annual IEEE/IFIP International Conference on, pages 403-414. IEEE, 2015.
24. M. A. Rumi, D. Hasan, et al. Cpu power consumption reduction in android smartphone. In Green Energy and Technology (ICGET), 2015 3rd International Conference on, pages 1-6. IEEE, 2015.
25. A. Shabtai, U. Kanonov, and Y. Elovici. Intrusion detection for mobile devices using the knowledge-based, temporal abstraction method. Journal of Systems and Software, 83(8):1524-1537, 2010.
26. A. Shabtai, U. Kanonov, Y. Elovici, C. Glezer, and Y. Weiss. "andromaly": a behavioral malware detection framework for android devices. Journal of Intelligent Information Systems, 38(1):161-190, 2012.
27. B. Shrestha, M. Mohamed, A. Borg, N. Saxena, and S. Tamrakar. Curbing mobile malware based on user-transparent hand movements.
28. M. Spreitzenbarth, F. Echtler, T. Schrek, F. C. Freiling, and J. Hoffman. Mobilesandbox: looking deeper into android applications. In Proc. the 28th ACM Symposium on Applied Computing (SAC), 2013.
29. J. Wei, E. Juarez, M. J. Garrido, and F. Pescador. Maximizing the user experience with energy-based fair sharing in battery limited mobile systems. Consumer Electronics, IEEE Transactions on, 59(3):690-698, 2013.
30. T.-E. Wei, C.-H. Mao, A. B. Jeng, H.-M. Lee, H.-T. Wang, and D.-J. Wu. Android malware detection via a latent network behavior analysis. In Trust, Security and Privacy in Computing and Communications (TrustCom), 2012 IEEE 11th International Conference on, pages 1251-1258. IEEE, 2012.
31. S. Y. Yerima, S. Sezer, and I. Muttik. High accuracy android malware detection using ensemble learning. IET Information Security, 2015.
32. Y. Zhou and X. Jiang. Dissecting android malware: Characterization and evolution. In Security and Privacy (SP), 2012 IEEE Symposium on, pages 95-109. IEEE, 2012.