DocumentCode :
3729420
Title :
Static code analysis and detection of multiple malicious Java applets using SVM
Author :
Sapana Y. Salunkhe;Tareek M. Pattewar
Author_Institution :
Department of Computer Engineering, North Maharashtra University, SES´s R. C. Patel Institute of Technology, Shirpur, India
fYear :
2015
Firstpage :
1538
Lastpage :
1542
Abstract :
An applet that performs an action against the will of the user who invoked it should be considered malicious. A malicious applet is applet that attacks the local system of a Web surfer. They can even seriously damage a Java user´s machine. The problem of malicious Java applets, that is currently not well addressed by existing work. We have developed a tool for malicious Java applets, which we call Jarhead. The approach is based on static code analysis. The approach extracts features from Java applets, and uses machine learning technique called support vector machine(SVM) to produce a tool. This approach is able to detect both known and previously-unseen real-world malicious applets.
Keywords :
"Java","Malware","Feature extraction","Fingerprint recognition","Browsers","Web pages"
Publisher :
ieee
Conference_Titel :
Green Computing and Internet of Things (ICGCIoT), 2015 International Conference on
Type :
conf
DOI :
10.1109/ICGCIoT.2015.7380711
Filename :
7380711
Link To Document :
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