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JavaScript code obfuscation has become a major technique used by malware writers to evade static analysis techniques. Over the past years, a number of dynamic analysis techniques have been proposed to detect obfuscated malicious JavaScript code at runtime. However, because of their runtime overheads, these techniques are slow and thus not widely used in practice. On the other hand, since a large quantity of benign JavaScript code is obfuscated to protect intellectual property, it is not effective to use the intrinsic features of obfuscated JavaScript code for static analysis purposes. Therefore, we are forced to distinguish between obfuscated and non-obfuscated JavaScript code so that we can devise an efficient and effective analysis technique to detect malicious JavaScript code. In this paper, we address this issue by presenting JSObfusDetector, a novel oneclass classifier ensemble to detect obfuscated JavaScript code. To construct the classifier ensemble, we apply a binary particle swarm optimization (PSO) algorithm, called ParticlePruner, on an initial ensemble of one-class SVM classifiers to find a subensemble whose members are both accurate and have diversity in their outputs. We evaluate JSObfusDetector using a dataset of obfuscated and non-obfuscated JavaScript code. The experimental results show that JSObfusDetector can achieve about 97% precision, 91% recall, and 94% F-measure.

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