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Robust Malware Detection using Residual Attention Network

Publication Type : Conference Paper

Publisher : IEEE

Source : Digest of Technical Papers - IEEE International Conference on Consumer Electronics, IEEE, p.1-6 (2021)

Url : https://www.techrxiv.org/articles/preprint/Robust_Malware_Detection_using_Residual_Attention_Network/13385291/1

Keywords : Convolutional neural network, Cyber security, Cybercrime, Deep learning, Residual attention

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electrical and Electronics

Year : 2021

Abstract : 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%.

Cite this Research Publication : Shamika Ganesan, R. Vinayakumar, Moez Krichen, Sowmya V., Roobaea Alroobaea, and Dr. Soman K. P., “Robust Malware Detection using Residual Attention Network”, in Digest of Technical Papers - IEEE International Conference on Consumer Electronics, 2021, pp. 1-6.

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