A New Feature Selection Method for Malcodes Detection

摘要：

Most of traditional antivirus systems fail to detect unknown malcodes or variants. Data mining method solves this problem as it classifies new malcodes by matching representative features. Feature selection is a key to apply data mining to successfully detect malcodes. In this paper, we propose a method, Weighted Information Gain (WIG), which can select effective features more correctly by combining the advantages of Information Gain with feature frequency. The experiment results demonstrate that the proposed method achieves high detection and accuracy rate.

作者: Xiaokang Zhang Jianmei Shuai

作者单位: Department of Automation,University of Science and Technology,Hefei 230027

会议类型: 国际会议

会议名称: The Fifth International Conference on Information Assurance and Security(第五届信息保障与安全国际会议)

会议地点: 西安

会议语种:英文

页码: 423-426

在线出版日期: 2009-08-18（万方平台首次上网日期，不代表论文的发表时间）