Online analytical model of massive malware based on feature clusting - Xiao-lin XU; Xiao-chun YUN; Yong-lin ZHOU; Xue-bin KANG (Tongxin xuebao)
In order to improve the effectiveness and efficiency of mass malicious code analysis,an online analytical model was proposed including feature space construction,automatic feature extraction and fast clustering.Our research focused on the law of malware behavior and code string distribution by dynamic and static techniques.In this model,a sample was described with its API and key code fragment.This model proposed a fast clustering approach to identify group samples that exhibit similar feature when applied this model to real-world malware collections.The result demonstrates that the proposed model is able to extract feature automatically,support streaming data clustering on large-scale,and achieve better precision.