Visual Profiling and Automated Classification of Malware Samples using Deep Learning
P. Subhash, Y. Sri Varsha, K. Saketh Reddy, B. Akshaya, S. Kalyani · Atlantis Highlights in Computer Sciences/Atlantis highlights in computer sciences · 2023
Information security is facing a significant issue due to the proliferation of malware programs.Malware analysis refers to the process of interpreting malicious software to determine its functionality and intent and assist in detection.Conventional methods, which rely on both static and dynamic analyses for malware identification and categorization, often strive to keep up with the everrising evolution of malware.Therefore, our proposal presents a thorough deep learning powered malware analysis system that is divided into three essential modules: data processing, feature extraction, detection, and classification.The data processing module handles converting binary data into grayscale photos specifically, includes an import feature, and skillfully extracts essential virus information.This module makes effective use of these extracted attributes to identify potentially suspicious samples and classify malware cases.The Detection and classification module, which completed the architecture, uses deep learning algorithms to identify malware and classify into respected families, resulting in a strong and proactive approach to cybersecurity.This paper contributes to the realm of enhanced cybersecurity by providing a method that not only enhances accuracy but also has the potential to adapt to emerging malware threats.