Mechanized Detection and Extraction of Malware Using Deep Learning Approaches - V. S. Jeyalakshmi (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Malware creation is developing a considerable dangerous to the individuals as well as an organization. Protecting against these risks is continually being processed by the digital protection cyber specialists. The obscurity of categorizing malware is high since it might take many patterns and is continually evolving. With the support of artificial intelligence can undoubtedly access the large information, neural networks can be able to deal this problem very easily. This research aims to furnish effective by applying convolutional neural network with multi-layers to handle the situations of using imbalanced datasets. The proposed model developed by applying a Convolutional Neural Network with multi layers performed best to categorize the malware with 96.25% of accuracy. Generally, the malware classification problem is eased by the approach of converting it to binary images and then classifying the generated images.