Author : Ms. Shubhangi Y. Chaware 1
Date of Publication :7th March 2016
Abstract: Malware is basically malicious software or programs which are a major challenge or major threats for the computer and different computer applications in the field of IT and cyber security. Traditional anti-viral packages and their upgrades are typically released only after the malware’s key characteristics have been identified through infection. The most common detection method is the signature based detection that makes the core of every commercial anti-virus program. To avoid detection by the traditional signature based algorithms, a number of stealth techniques have been developed by the malware writers. The inability of traditional signature based detection approaches to catch these new breed of malwares has shifted the focus of malware research to find more generalized and scalable features that can identify malicious behavior as a process instead of a single static signature. The goal of proposed work is to create a hybrid model for feature selection and Malware categorization. Feature selection is important issue in Malware categorization. The selection of feature in attack attribute sand normal traffic attribute is challenging task. For the test of our hybrid method, we used DARPA KDDCUP99 dataset. This data set basically set of network Malware and host Malware data. This data provided by UCI machine learning website. Our proposed method compare with exiting ISMCS, HC and KM technique and getting better result such as F-measure, precision and recall value.
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