Image-based malware analysis for enhanced IoT security in smart cities

Abstract

To develop safer smart city solutions, it is crucial to investigate new technologies for malware analysis and detection to enhance existing malware prevention systems. Deep learning (DL) techniques have surpassed conventional machine learning as the dominant method for network security; therefore, it is crucial for researchers to utilise DL techniques to address the velocity, volume, and complexity of modern malware, as these approaches can effectively handle large amounts of data and extract representative information robustly. This study investigates the application of deep learning techniques, specifically different autoencoders (AEs), to improve malware analysis and prevention in Internet of Things (IoT)-based smart cities. This research evaluates different convolutional neural network based AE structures and aims to establish a robust malware analysis method by focusing on image classification. By experimenting on both greyscale and RGB malware imagery datasets, it has been demonstrated that variational AE can not only detect nearly all malware but also illustrate the generalisability and efficacy of AE in addressing malware threats. The study systematically evaluates different AE configurations, and this comparative analysis can inspire further research into deep learning techniques for IoT security measures.