Dr. Kamalakanta Sethi
A Novel Malware Analysis Framework for Malware Detection and Classification using Machine Learning Approach
1/4/2018
Proceedings of the 19th International Conference on Distributed Computing and Networking
ACM Digital Library
Abstract

Nowadays, the digitization of world is under a serious threat due to emergence various new and complex malware every day. Due this, traditional signature-based methods for detection effectively become an obsolete method. The efficiency machine learning techniques in context malwares has been proved by state-of-the-art research works. In this paper, we have proposed framework detect classify different files (e.g., exe, pdf, php, etc.) as benign malicious using two level classifier namely, Macro (for malware) Micro classification Trojan, Spyware, Ad-ware, etc.). Our solution uses Cuckoo Sandbox generating static dynamic analysis report executing sample virtual environment. addition, novel feature extraction module developed which functions based on static, behavioral network reports generated Sandbox. Weka Framework used develop models training datasets. experimental results shows high rate algorithms

Keywords
Advanced Malware Detection TechniquesDigital and Cyber ForensicsNetwork Security and Intrusion DetectionSignal ProcessingInformation SystemsComputer Networks and Communications
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