Application of Deep Belief Networks for opcode based malware detection

Proceedings of the International Joint Conference on Neural Networks

Volumn 2016-October, Issue , 2016, Pages 3901-3908

  Ding, Yuxin ^a   Chen, Sheng ^a   Xu, Jun ^b  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b UNIVERSITY OF TEXAS   (United States)

Author keywords

DBN; Deep Learning; Deep neural Nets; Malware detection; Security

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER CRIME; DECISION TREES; INPUT OUTPUT PROGRAMS; LEARNING SYSTEMS; MALWARE; NEAREST NEIGHBOR SEARCH; PATTERN RECOGNITION; SUPPORT VECTOR MACHINES; TREES (MATHEMATICS);

CLASSIFICATION ALGORITHM; DEEP BELIEF NETWORKS; DEEP LEARNING; DEEP NEURAL NETS; K NEAREST NEIGHBOR ALGORITHM; MALWARE DETECTION; OVER FITTING PROBLEM; SECURITY;

DATA MINING;

EID: 85007164413     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2016.7727705     Document Type: Conference Paper

References (27)

1. G. McGraw, G. Morrisett, "Attacking malicious code: report to the infosec research council", IEEE Softw, vol. 17, pp. 33-41, 2002.
2. E. Filiol, "Malware pattern scanning schemes secure against black box analysis", J. Comput. Virol, vol. 2, pp. 35-50, 2006.
3. M. Christodorescu, S. Jha, "Testing malware detectors", in ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA'04), Boston, USA, 2004, pp. 34-44.
4. T. Brosch, M. Morgenstern, "Runtime packers: the hidden problem", In Proceedings of Black Hat, Las Vegas, USA, 2006, pp. 325-334.
5. A. Shabtai, R. Moskovitch, Y. Elovici, C. Glezer, "Detection of malicious code by applying machine learning classifiers on static features - a state-of-the-art survey", Information Security Technical Report. vol. 14, pp. 16-29, 2009.
6. M. G. Schultz, E. Eskin, E. Zadok, S. J. Stolfo, "Data mining methods for detection of new malicious executables", In Proceedings of the IEEE Symposium on Security and Privacy, Oakland USA, 2001, pp. 38-49.
7. J. Z. Kolter, M. A. Maloof, "Learning to detect malicious executables in the wild", In Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. New York, USA, 2004, pp. 470-8.
8. Y. Ding Y., X. Yuan, D. Zhou, L. Dong, Z. An, "Feature representation and selection in malicious code detection methods based on static system calls", Computers & Security, vol. 30, pp. 514-524, 2011.
9. J. Wang, P. Deng, Y. Fan, L. Jaw, Y. Liu, "Virus detection using data mining techniques", In Proceedings of IEEE International Conference on Security Technology, 2003, pp. 71-76.
10. O. Henchiri, N. Japkowicz, "A feature selection and evaluation scheme for computer virus detection", In Proceedings of ICDM-2006, Hong Kong; 2006, pp. 891-95.
11. R. Moskovitch, D. Stopel, C. Feher, N. Nissim, Y. Elovici, "Unknown malcode detection via text categorization and the imbalance problem", In IEEE Intelligence and Security Informatics, Taiwan; 2008, pp. 156-61.
12. R. Moskovitch, N. Tzachar, E. Berger, M. Gitelman M, S. Dolev, et al., "Unknown malcode detection using OPCODE representation", In European conference on intelligence and security informatics, Esbjerg, Denmark, 2008, pp. 204-215.
13. R. Sarikaya, G. E. Hinton, and A. Deoras, "Application of deep belief networks for natural language understanding", IEEE Transactions on Audio, Speech, and Language Processing, vol. 22, pp. 778-784, 2014.
14. G. E. Dahl, M. Ranzato, Momamed A., and Hinton G. E., "Phone Recognition with the Mean-Covariance Restricted Boltzmann Machines", in Advances in Neural Information Processing Systems NIPS. Cambridge, USA, 2010, pp. 1-8.
15. D. Erhan, Y. Bengio, A. Courville, P. Manzagol, and P. Vincent, "Why does unsupervised pre-training help deep learning?", J. Mach. Learn. Res., vol. 11, pp. 625-660, 2010.
16. G. E. Hinton, S. Osindero, and Y. The, "A fast learning algorithm for deep belief nets", Neural Comput., vol. 18, pp. 1527-1554, 2006.
17. A. Itamar, C. Derek, and P. Thomas, "Deep machine learning-a new frontier in artificial intelligence research", IEEE computational intelligence magazine, vol. 11, pp. 13-18, 2010.
18. Y. Ding, W. Dai, S. Yan, "Control flow-based opcode behavior analysis for malware detection", Computers & Security, vol. 44, pp. 231:64-82, 2014.
19. Hex-Rays SA, "IDA pro introduction", Available from: http://www.hexrays.com/products.shtml/ (Accessed 23.3.2010).
20. Peid, "Peid v0.94." Available From: http://www.peid.info/ (Accessed 23.3.2010).
21. S. Igor, B. Felix, U. P. Xabier, "Opcode sequences as representation of executables for data-mining-based unknown malware detection", Information Science, vol. 231, pp. 64-82, 2013..
22. Z. Zhao, J. Wang, J. Bai, "Malware detection method based on the control-flow construct feature of software", IET Information Security, vol. 8, pp. 18-24, 2014.
23. G. Hinton, and R. Salakhutdinov, "Reducing the dimensionality of data with neural networks", Science, vol. 313, pp. 504-507, July 2006.
24. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, "Greedy layerwise training of deep networks", in Proc. NIPS, 2006, pp. 1-8.
25. T. M. Mitchell. Machine Learning, McGraw-Hill, 1997.
26. S. Amari, S. Wu, "Improving support vector machine classifiers by modifying kernel functions", Neural Networks, Vol. 12, pp. 783-789, 1999.
27. S. Garner, "Weka: The Waikato environment for knowledge analysis", in Proceedings of the 1995 New Zealand Computer Science Research Students Conference, New Zealand, 1995, pp. 57-64.