Abstract:
Cyber-attacks have been on the rise especially after the explosive widespread of social networking as it gives cyber criminals a way to break into other’s computers and manipulate personal and sensitive data. Many different techniques have been used in the past to minimize the occurrences of cyber-attacks. These techniques focused primarily on traffic in order to look for malicious activity. This research proposes a methodology that can classify malware family on the basis of statistical features. To keep original features, we use Variance, ¾ quartile method to eliminate the un-important features. Forward selection wrapper method in feature selection is used to find out best features. To validate our proposed methodology, K Nearest Neighbor and Decision Tree is used as classifier and very promising results are achieved.
