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Malware Behaviour Visualization

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The number of unique malware variants released each year is on the rise. Researchers may often need to use manual static and dynamic analysis to study new malware samples. Manual analysis of malware samples takes time. The more time taken to analyse a malware sample, the larger the damage that a malware can inflict. A lot of techniques have been devised by researchers to facilitate malware analysis and one of them is through malware visualization. Malware visualization is a field that focuses on representing malware features in the form of visual cues or images. This could be used to convey more information about a particular malware. Existing malware visualization techniques lack focus in visualizing malware behaviour in such a way that could enable better analysis of malware samples. In this paper, a new technique for malware visualization called ‘Malware Behaviour Image’ is presented. From the test results, the proposed technique is able to accurately capture and highlight malicious behaviour of malware samples, and can be used for malware analysis, detection and identification of malware variants.
Title: Malware Behaviour Visualization
Description:
The number of unique malware variants released each year is on the rise.
Researchers may often need to use manual static and dynamic analysis to study new malware samples.
Manual analysis of malware samples takes time.
The more time taken to analyse a malware sample, the larger the damage that a malware can inflict.
A lot of techniques have been devised by researchers to facilitate malware analysis and one of them is through malware visualization.
Malware visualization is a field that focuses on representing malware features in the form of visual cues or images.
This could be used to convey more information about a particular malware.
Existing malware visualization techniques lack focus in visualizing malware behaviour in such a way that could enable better analysis of malware samples.
In this paper, a new technique for malware visualization called ‘Malware Behaviour Image’ is presented.
From the test results, the proposed technique is able to accurately capture and highlight malicious behaviour of malware samples, and can be used for malware analysis, detection and identification of malware variants.

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