JavaScript Malicious Script Detection Algorithm Based on Multi-class Features
FU Lei-Peng, ZHANG Han, HUO Lu-Yang
College of Computer and Control Engineering, Nankai University, Tianjin 300071 Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300071
Abstract：Aiming at features of different levels in the script sample set, such as obfuscation, statistics and semantics, a malicious JavaScript script detection algorithm based on multi-class feature is proposed. The JavaScript analysis system, JavaScript codes analysis and detection, is implemented. The obfuscation features of the JavaScript are extracted and the obfuscated scripts are analyzed and deobfuscated by C4.5 algorithm. The static statistical features of the JavaScript are extracted, and according to the semantics, the JavaScript is serialized. Dangerous sequence tree is generated by the proposed algorithm to extract the dangerous sequence features of the malicious JavaScript. Three types of features are used as the input. The probabilistic neural network with strong ability to adapt to non-uniformity and the increasing quantity of the input samples is applied to construct the classifier for the detection of malicious JavaScript. The experimental results show that the proposed algorithmhas better detection accuracy and stability.
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2015, Vol. 28 
