Thesis (Selection of subject)Thesis (Selection of subject)(version: 368)
Thesis details
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Computational Intelligence for Malware Classification
Thesis title in Czech: Výpočetní inteligence pro klasifikaci malware
Thesis title in English: Computational Intelligence for Malware Classification
Key words: malware, strojové učení, klasifikace
English key words: malware, machine learning, classification
Academic year of topic announcement: 2014/2015
Thesis type: diploma thesis
Thesis language: angličtina
Department: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Supervisor: Mgr. Martin Pilát, Ph.D.
Author: hidden - assigned and confirmed by the Study Dept.
Date of registration: 03.03.2015
Date of assignment: 03.03.2015
Confirmed by Study dept. on: 19.03.2015
Date and time of defence: 08.09.2015 09:30
Date of electronic submission:29.07.2015
Date of submission of printed version:30.07.2015
Date of proceeded defence: 08.09.2015
Opponents: doc. RNDr. Pavel Surynek, Ph.D.
 
 
 
Guidelines
Automatická klasifikace škodlivého software poskytuje zajímavé výzvy pro metody strojového učení a výpočetní inteligence. Je potřeba vybrat vhodné příznaky z binárních souborů. To s sebou mimojiné nese i nutnost pracovat s velkými objemy dat.

Student se seznámí s přístupy pro klasifikaci složitých nestrukturovaných dat a pokusí se navrhnout nové přístupy použitelné pro klasifikaci malware. Navržené postupy naimplementuje a otestuje.
References
[1] Kinable, Joris, and Orestis Kostakis. "Malware classification based on call graph clustering." Journal in computer virology 7.4 (2011): 233-245.
[2] Tian, Ronghua, Lynn Margaret Batten, and S. C. Versteeg. "Function length as a tool for malware classification." Malicious and Unwanted Software, 2008. MALWARE 2008. 3rd International Conference on. IEEE, 2008.
[3] Cesare, Silvio, and Yang Xiang. "Classification of malware using structured control flow." Proceedings of the Eighth Australasian Symposium on Parallel and Distributed Computing-Volume 107. Australian Computer Society, Inc., 2010.
[4] Haykin, Simon, "Neural Networks: A Comprehensive Foundation", 2nd ed., Prentice Hall, 1999.
[5] Bishop, Christopher M., "Pattern Recognition and Machine Learning", Springer, 2006.
 
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