JavaScript Malicious Codes Analysis Based on Naive Bayes Classification

Given the security threats of JavaScript malicious codes attacks in the Internet environment, this paper presents a method that uses the Naive Bayes classification to analyze JavaScript malicious codes. The method uses many malicious and normal sample data, and trains the classifier using extended A...

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Vydané v:	2014 Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing s. 513 - 519
Hlavní autori:	Yongle Hao, Hongliang Liang, Daijie Zhang, Qian Zhao, Baojiang Cui
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.11.2014
Predmet:	Accuracy Browsers Feature extraction JavaScript; malicious codes; Naive Bayes; API symbol features Noise measurement Security Training Web pages
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Shrnutí:	Given the security threats of JavaScript malicious codes attacks in the Internet environment, this paper presents a method that uses the Naive Bayes classification to analyze JavaScript malicious codes. The method uses many malicious and normal sample data, and trains the classifier using extended API symbol features with a high degree of predictability of malicious codes, which contain variable names, function names, string constants and comments extracted from the JavaScript codes. Experiments show that the analysis method of JavaScript malicious codes is effective and achieves high accuracy.
DOI:	10.1109/3PGCIC.2014.147