Automatic Benchmark Generation Framework for Malware Detection

作者：Liang, Guanghui; Pang, Jianmin^*; Shan, Zheng; Yang, Runqing; Chen, Yihang

来源：Security and Communication Networks, 2018, 2018: UNSP 4947695.

DOI：10.1155/2018/4947695

To address emerging security threats, various malware detection methods have been proposed every year. Therefore, a small but representative set of malware samples are usually needed for detection model, especially for machine-learning-based malware detectionmodels. However, currentmanual selection of representative samples fromlarge unknown file collection is labor intensive and not scalable. In this paper, we firstly propose a framework that can automatically generate a small data set formalware detection. With this framework, we extract behavior features from a large initial data set and then use a hierarchical clustering technique to identify different types of malware. An improved genetic algorithm based on roulette wheel sampling is implemented to generate final test data set. The final data set is only one-eighteenth the volume of the initial data set, and evaluations show that the data set selected by the proposed framework is much smaller than the original one but does not lose nearly any semantics.

• 出版日期2018
• 单位浙江大学