Ensemble based categorization and adaptive model for malware detection

Ahmad Zabidi, Muhammad Najmi and Maarof, Mohd Aizaini and Zainal, Anazida (2011) Ensemble based categorization and adaptive model for malware detection. 2011 7th International Conference on Information Assurance and Security (IAS), 978-1-. pp. 80-85.

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Abstract

Malware, a term which was derived from two words; malicious software has caused many problem to the computer users throughout the world. Previously was known as many names; trojan, virus, worms, dialers and many others, thid potientially unwanted software simply labeled as malware. Malware is a software, which works as any other benigh software, but was designed to accomplish the goal of its writers. It was written to exploit the vulnerability of the target victim’s operating system or application. Previously was a primitive and easy to detect, it evolves to a sophisticated and professionally written piece of software. Current malware detection method involved string search algorithm which based on the pattern detection. This may include the use of signature based method. In this paper, we propose an ensemble categorization by using ensemble classification and clustering together with adaptive learning model.

Item Type:	Article (UNSPECIFIED)
Additional Information:	4969/15860 (Print ISBN: 978-1-4577-2154-0, DOI: 10.1109/ISIAS.2011.6122799)
Uncontrolled Keywords:	adaptive , ensemble , machine learning , malware , soft computing
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Mr Muhammad Najmi Ahmad Zabidi
Date Deposited:	15 Mar 2012 13:53
Last Modified:	26 Jul 2012 09:49
URI:	http://irep.iium.edu.my/id/eprint/15860

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