Deep Learning based Malware Analysis, Prediction and Prevention

There has been a sharp increase in malware growth recently. Malware has evolved to become one of the most serious cybersecurity risks due to its rising prevalence and sophistication. When malware is introduced into a computer system, an attacker gains complete or limited access to the system’s crucial data. In this study, the idea of foretelling if an executable or its picture file is malicious is explored. This initiative offers fresh approaches and techniques for malware detection and categorization to other researchers. Malware detection and classification software that can transform an executable file into its associated bytes and asm files and then into images is presented. As a result, this study broadens the dataset and creates a balanced dataset, which includes examples of both benign software and malicious software. For more diverse malware detection trials, the dataset can be enlarged. In order to determine the most effective way for malware detection and categorization, this research study analyses machine learning and deep learning algorithms. The proposed malware detection method yields better results with a high degree of accuracy and precision and is trustworthy and effective.