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