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%.