Malware is a computer program that can replicate itself and cause potential damage in data files. The high speed of the computers and networks increased the virus spread. To avoid the virus infection and the data loss, it is important to use an efficient and effective method for virus detection. This paper proposes an approach for malware detection based on the evolving clustering method. The proposed approach effectively combined the information gain method as a feature selector with the evolving clustering method as evolving learning classifier. Based on the experimental results, the proposed malware detection approach proved its capability to detect the malware by decreasing the false positive rate to 1% while increasing the level of accuracy to 99%.

Key words: Malware detection, network security, intelligent classification, information gain.