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%.

Recent advancements in Cyber Security has amalgamated the strengths of Artificial Intelligence and Human Intelligence for Intrusion Detection. The colossal increase in the volume of new malwares generated everyday and the constant risk of zero day attacks demand research for a robust malware detection system. Significant research has gone into exploring the use of Machine Learning and Convolutional Neural Networks. However, to cater to the complexity of such a data-intensive environment generalizability of malware detection becomes the key to creating a successful anti-malware system. There has been a transition from using Malware byte information for Machine Learning and Deep Learning based methods to using an Image based Intrusion Detection system for better assessment of the malware file. Though using Convolutional Neural Networks(CNNs) have helped in capturing local features, Attention based mechanisms play a vital role in detecting polymorphic malware. Hence, 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%.

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