A New Malware Classification Framework based on Deep Learning Algorithms

The increasing sophistication and prevalence of malware pose a significant threat to digital security, necessitating the development of robust and efficient classification methods. This research proposes a novel approach to malware classification utilizing deep learning techniques applied to image datasets. By representing malware samples as images, we aim to leverage the power of convolutional neural networks (CNNs) for automatic feature extraction and classification. This innovative approach not only enhances the detection accuracy but also provides a visual representation that aids in understanding the characteristics and patterns of different malware families. The study explores the feasibility and effectiveness of this image-based deep learning model for malware classification.

Keywords: malware Classification, Convolutional Neural Networks, Inception, Mobile net, Deep Learning, Artificial Intelligence.