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Robust Malware Detection using Residual Attention Network
  • +2
  • Shamika Ganesan ,
  • vinayakumar R ,
  • Moez Krichen ,
  • Sowmya V ,
  • Soman KP
Shamika Ganesan
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vinayakumar R
Prince Mohammad Bin Fahd University

Corresponding Author:[email protected]

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Moez Krichen
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In this paper, we explore the use of an attention based mechanism known as Residual Attention for malware detection and compare this with existing CNN based methods and conventional Machine Learning algorithms with the help of GIST features. The proposed method outperformed traditional malware detection methods which use Machine Learning and CNN based Deep Learning algorithms, by demonstrating an accuracy of 99.25%.