An Empirical Analysis of Image-Based Learning Techniques for Malware Classification

Author:

Prajapati Pratikkumar,Stamp Mark

Publisher

Springer International Publishing

Reference44 articles.

1. Adware:win32/hotbar. https://www.microsoft.com/en-us/wdsi/threats/malware-encyclopedia-description?Name=Adware:Win32/Hotbar&threatId=6204.

2. Austin, Thomas H., Eric Filiol, Sébastien Josse, and Mark Stamp. 2013. Exploring hidden Markov models for virus analysis: A semantic approach. In 46th Hawaii international conference on system sciences, HICSS 2013, Wailea, HI, USA, January 7–10, 2013, 5039–5048. IEEE Computer Society.

3. Backdoor:win32/cycbot.g. https://www.microsoft.com/en-us/wdsi/threats/malware-encyclopedia-description?Name=Backdoor:Win32/Cycbot.G.

4. Backdoor:win32/zegost.ad. https://www.microsoft.com/en-us/wdsi/threats/malware-encyclopedia-description?Name=Backdoor%3AWin32%2FZegost.AD.

5. Baysa, Donabelle, Richard M. Low, and Mark Stamp. 2013. Structural entropy and metamorphic malware. Journal of Computer Virology and Hacking Techniques 9 (4): 179–192.

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