An Artificial Intelligence Approach for Malware Detection Using Deep Learning

Computer users, organizations, and governments are all seeing an exponential development in the number of malware assaults. When it comes to determining the nature of unknown forms of malware, dynamic and static analyses of malware signatures and behavior patterns used by modern malware detection technologies are both inefficient and time intensive. The purpose of these investigations is to determine the types of malwares that are already known. Machine learning algorithms (MLAs) have been more popular as a method for successful malware analysis in recent years. This article investigates both conventional MLAs and deep learning algorithms or the detection using public and private datasets, classification, and categorization of malware. We provide a novel method to image processing, which is characterized by parameters that are optimal for the designs of deep learning conventional neural networks (DLCNN). A technique for the effective visual detection of malware is presented in its entirety in this study. The approach makes use of a framework for real-time deployments that is both scalable and hybrid in its fundamental makeup. The utilization of visualization and DL architectures for previous approaches (static, dynamic, and image processing-based hybrid) in an environment containing massive data is at the heart of a recently developed and significantly improved method for successfully detecting zero-day malware. In conclusion, the findings of the simulations revealed that the performance produced by the recommended DLCNN was better than the existing models. This was proved by the fact that the DLCNN outperformed the present models.