Feature selection and clustering for malicious and benign software characterization

Malware or malicious code is design to gather sensitive information without knowledge or permission of the users or damage files in the computer system. As the use of computer systems and Internet is increasing, the threat of malware is also growing. Moreover, the increase in data is raising difficulties to identify if the executables are malicious or benign. Hence, we have devised a method that collects features from portable executable file format using static malware analysis technique. We have also optimized the important or useful features by either normalizing or giving weightage to the feature. Furthermore, we have compared accuracy of various unsupervised learning algorithms for clustering huge dataset of samples. So once the clusters are created we can use antivirus (AV) to identify one or two file and if they are detected by AV then all the files in cluster are malicious even if the files contain novel or unknown malware; otherwise all are benign