Malware Testing Analysis using Fisher Linear Algorithm
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2018-06-19T10:11:17Z
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Abstract
Malware has been recognized as one of the major security threats in the Internet. Previous researches have mainly focused on malware's internal activity in a system. However, it is crucial that the malware analysis extracts a malware's external activity toward the network to correlate with a security incident. We propose a novel way to analyze malware: focus closely on the malware's external (i.e., network) activity. A malware sample is executed on a sandbox that consists of a real machine as victim and a virtual Internet environment. Since this sandbox environment is totally isolated from the real Internet, the execution of the sample causes no further unwanted propagation. The sandbox is configurable so as to extract specific activity of malware, such as scan behaviors. We implement a fully automated malware analysis system with the sandbox, which enables us to carry out the large-scale malware analysis. We present concrete analysis results that are gained by using the proposed system.
Malware analysis is a process to perform analysis of malware and how to study the components and behavior of malware.
Malware analysis forms a critical component of cyber defense mechanism. In the last decade, lot of research has been done, using machine learning methods on both static as well as dynamic analysis. In this paper, we compare various machine-learning techniques used for analyzing malwares.
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SANGHEETHA G, SHIVANI P, ROHIT V, SHWETA BARUA, 1NH14CS115, 1NH14CS120, 1NH14CS753, 1NH14CS756