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 malwares 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.
- Kai Huang , Yanfang Ye , Qinshan Jiang “ISMCS: An Intelligent Instruction Sequence based Malware Categorization Sy stem” the National Science Foundation of China
- Tawfeeq S. Barhoom, Hanaa A. Qeshta “Adaptive Worm Detection Model Based on Multi classifiers ”Palestinian International Conference on Information and Communication Technology 2013, Pp57-65.
- StanislavPonomarev, Jan Durand, Nathan Wallace, Travis Atkison” Evaluation of Random Projection for Malware Classification” 2013, Pp 68-73
- Aiman A. Abu Samra, KangbinYim , Osama A. Ghanem, “ Analysis of Clustering Technique in Android Malware Detection” Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing 2013, Pp 729-733
- Jonghoon Kwon, Heejo Lee ,” BinGraph: Discovering Mutant Malware using Hierarchical Semantic Signatures” 7th International Conference on Malicious and Unwanted Software, 2012, Pp 104-111.
- P.R.LakshmiEswari , N.Sarat Chandra Babu “A Practical Business Security Framework to Combat Malware Threat “World Congress on Internet Security, 2012, Pp 77-80
- Ahmed F.Shosha, Chen-Ching Liu, PavelGladyshev, Marcus Matten “Evasion-Resistant Malware Signature Based on Profiling Kernel Data Structure Objects” 2012, 7th International Conference on Risks and Security of Internet and Systems (CRiSIS)
- Vinod P., V.Laxmi , M.S.Gaur , GrijeshChauhan “MOMENTUM :MetamOrphicMalware Exploration Techniques Using MSAsignatures”International Conference on Innovations in Information Technology (IIT), 2012, Pp 232-237.
- HiraAgrawal, Lisa Bahler, Josephine Micallef, Shane Snyder, and AlexandrVirodov “Detection of Global, Metamorphic Malware Variants Using Control and Data Flow Analysis” 2013 ,Pp 1-6.
- Yanfang Ye, Tao Li, Qingshan Jiang, and Youyu Wang,” CIMDS: Adapting Postprocessing Techniques of Associative Classification for Malware Detection” IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 40, NO. 3 ,2010,Pp 298-307