Decision-Making Techniques and Methods for Sustainable Technological Innovation

12Decision-Making in Malware Detection Through Advanced Imaging Techniques

Rohan Alroy B., Shivaprakash S. J., Akshat Chauhan and Jayasudha M.*

School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

Abstract

One of the most important computer security tasks is the detection of malware and benignware pictures. In this research, we offer a revolutionary deep learning– based method for identifying malicious or benign software pictures. We make advantage of a publicly accessible dataset that includes lots of malware and benignware images. In order to develop features that can distinguish between malware and benignware photos, we propose training a deep neural network on this dataset. We assess the effectiveness of our strategy using a number of indicators, including recall, accuracy, and precision. Our findings show that our method is highly accurate at identifying malicious and benign software images. Additionally, we compare our method to other cutting-edge approaches and show that it is superior in terms of accuracy and detection rate. Our suggested strategy aligns with the principles of Industry 5.0 by enhancing computer system security through an efficient and sustainable tool for identifying software threats, thereby fostering a secure technological environment for sustainable innovation.

Keywords: Malware classification, deep learning, image classification, ensembling, security

12.1 Introduction

Computer systems are still seriously ...