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Please use this identifier to cite or link to this item: http://idr.iitbbs.ac.in/jspui/handle/2008/2001
Title: Novel malware analysis framework for malware detection and classification using machine learning approach
Authors: Sethi K.
Chaudhary S.K.
Tripathy B.K.
Bera P.
Keywords: Cuckoo Sandbox
Malware Classification
Malware Detection
SMO
Static and Dynamic Analysis
Issue Date: 2018
Citation: 3
Abstract: Nowadays, the digitization of the world is under a serious threat due to the emergence of various new and complex malware every day. Due to this, the traditional signature-based methods for detection of malware effectively become an obsolete method. The efficiency of the machine learning techniques in context to the detection of malwares has been proved by state-of-The-Art research works. In this paper, we have proposed a framework to detect and classify different files (e.g., exe, pdf, php, etc.) as benign and malicious using two level classifier namely, Macro (for detection of malware) and Micro (for classification of malware files as a Trojan, Spyware, Adware, etc.). Our solution uses Cuckoo Sandbox for generating static and dynamic analysis report by executing the sample files in the virtual environment. In addition, a novel feature extraction module has been developed which functions based on static, behavioral and network analysis using the reports generated by the Cuckoo Sandbox. Weka Framework is used to develop machine learning models by using training datasets. The experimental results using the proposed framework shows high detection rate and high classification rate using different machine learning algorithms In ICDCN '18: 19th International Conference on Distributed Computing and Networking, January 4-7, 2018, Varanasi, India. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3154273.3154326 � 2018 ACM. 978-1-4503-6372-3/18/01. . . $15.00.
URI: http://dx.doi.org/10.1145/3154273.3154326
http://10.10.32.48:8080/jspui/handle/2008/2001
Appears in Collections:Research Publications

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