Robust Malware Detection using Residual Attention Network

Recent advancements in Cyber Security have amalgamated the strengths of Artificial Intelligence and Human Intelligence for Intrusion Detection. The colossal increase in the volume of new malware 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 (CNNs). 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 CNNs have helped in capturing local features, Attention based mechanisms play a vital role in detecting structural changes in malware. In this paper, we have explored the use of Residual Attention for malware detection and have compared this with the existing CNN based methods and conventional Machine Learning algorithms using GIST features. The proposed method could efficiently focus the 'attention' to precisely those sections of the malware which were significant in being distinguished from benign files, thus reducing the False Positives which is primary from the Accessibility point of view in Cyber Security. The method is robust against structural changes in malware and has outperformed traditional malware detection methods by demonstrating an accuracy of 99.25%.