Malware detection on mobile devices using distributed machine learning

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2010, Pages 4348-4351

  Shamili, Ashkan Sharifi ^a   Bauckhage, Christian ^a   Alpcan, Tansu ^b  

^a Rheinische Friedrich Wilhelms Universität   (Germany)

^b TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; DISTRIBUTED LEARNING; DISTRIBUTED MACHINE LEARNING; INDIVIDUAL COMPONENTS; LIGHT WEIGHT; MALICIOUS SOFTWARE; MALWARE DETECTION; MALWARES; MOBILE USERS; PRIVACY PRESERVING; REALITY MININGS; STATISTICAL CLASSIFICATION; SUPPORT VECTOR MACHINE ALGORITHM; SYSTEM MONITORS; USAGE PATTERNS;

COMPUTER CRIME; GLOBAL SYSTEM FOR MOBILE COMMUNICATIONS; LEARNING SYSTEMS; LOCOMOTIVES; MOBILE DEVICES; PATTERN RECOGNITION; PORTABLE EQUIPMENT; RAILROAD CARS;

MATHEMATICAL MODELS;

EID: 78149476513     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2010.1057     Document Type: Conference Paper

References (13)

1. T. Alpcan and C. Bauckhage. A discrete-time parallel update algorithm for distributed learning. In Proc. Int. Conf. on Pattern Recognition, 2008.
2. T. Alpcan and C. Bauckhage. A distributed machine learning framework. In Proc. IEEE Conf. on Decision and Control, 2009.
3. T. Alpcan, C. Bauckhage, and A.-D. Schmidt. A probabilistic diffusion scheme for anomaly detection on smartphones. In Proc. Workshop on Information Security Theory and Practices, 2010.
4. A. Bose, X. Hu, K. G. Shin, and T. Park. Behavioral detection of malware on mobile handsets. In Proc. Int. Conf. on Mobile Systems, Applications And Services, 2008.
5. R. Bye, K. Luther, S. A. Camtepe, T. Alpcan, S. Albayrak, and B. Yener. Decentralized detector generation in cooperative intrusion detection systems. In Proc. Int. Symp. on Stabilization, Safety, and Security of Distributed Systems, 2007.
6. N. Eagle and A. S. Pentland. Reality mining: Sensing complex social systems. Personal and Ubiquitous Computing, 10(4):255-268, 2006.
7. I. D. Gesu, G. L. Bosed, and J. H. Friedman. Intruders pattern identification. In Proc. Int. Conf. on Pattern Recognition, 2008.
8. M. A. E. Maloof. Machine Learning and Data Mining for Computer Security. Springer, 2006.
9. M. Reif, M. Goldstein, A. Stahl, and T. M. Breuel. Anomaly detection by combining decision trees and parametric densities. In Proc. Int. Conf. on Pattern Recognition, 2008.
10. K. Rieck, T. Holz, C. Willems, P. Duessel, and P. Laskov. Learning and classification of malware behavior. In Proc. Int. Conf. on Detection of Intrusions and Malware, and Vulnerability Assessment, 2008.
11. K. Wang and S. J. Stolfo. Anomalous payload-based network intrusion detection. In Proc. Int. Symp. on Recent Advances in Intrusion Detection, 2004.
12. N. Wu and J. Zhang. Factor-analysis based anomaly detection and clustering. Decision Support Systems, 42:375-389, 2006. (Pubitemid 44308263)
13. D. Yang and H. Qi. A network intrusion detection method using independent component analysis. In Proc. Int. Conf. on Pattern Recognition, 2008.