Paper Title :Analysis And Detection Of Multiple Malicious Java Applets Based On Static Code Analysis: A Survey

Author :Sapana y. Salunkhe, Tareek M. Pattewar

Article Citation :Sapana y. Salunkhe ,Tareek M. Pattewar , (2015 ) " Analysis And Detection Of Multiple Malicious Java Applets Based On Static Code Analysis: A Survey " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 34-38, Volume-3, Issue-2

Abstract : Malicious web content is a major attack vector on the Internet. Typically, the attacker's goal is to install and run a piece of malware on the victim's computer. The research community has not given to this problem the attention it deserves, and, the approaches to the detection of malicious Java applets are based either on simple signatures or on the use of honey-clients, which are both easily avoided. The proposed novel approach to the detection of malicious Java applets based on static code analysis. The approach extracts a number of features from Java applets, and uses supervised machine learning to produce a classifier. This approach is able to detect both known and previously-unseen real-world malicious applets. We can extend our analysis to consider multiple applets that appear on the same web page together. Keywords- Code obfuscation, Disassembly, Honeyclients, Malicious Web Content

Type : Research paper

Published : Volume-3, Issue-2

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