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Around 800,000 people fall prey to cyberattacks annually, most often by “malware”. Malware has the potential to become a destructive weapon in Cyber-world. It is a difficult task to manually thwart an assault by malware. It is crucial to properly categorize malware binaries in order to identify their origins. Furthermore, malware structure discovery through basic feature extraction approaches are time-consuming and challenging. Malware classification was previously solved using naive machine learning approaches like support vector machine (SVM) and extreme gradient boosting (XGBoost). Recently, deep learning (DL) has shown to be impactful in finding malicious patterns. Without DL, analysis of the vast amounts of available data tends to impossible. Existing methods (e.g., transfer learning, fusion methodology, ensemble learning) may not be effective on actual malware binary files. Moreover, some single image …