CNN-Based Transfer Learning Approach for Cross-Platform IoT Malware Detection

Abstract

The increasing prevalence of Internet of Things (IoT) devices has raised significant concerns regarding their security. Malware attacks on these devices can lead to severe consequences, including data breaches, privacy violations, and system failures. This paper proposes a novel approach for detecting malware in cross-architecture IoT devices using deep learning with Convolutional Neural Networks (CNNs). The proposed methodology involves converting binary files into grayscale images and utilizing a CNN model for feature extraction and classification. The model's performance was evaluated on a dataset comprising malware samples from various IoT devices, achieving an accuracy of 97%. The results demonstrate the effectiveness of the proposed approach, outperforming existing methodologies in detecting malware in cross-architecture IoT environments. The model's robustness and adaptability across various malware samples highlight its potential as a valuable tool for enhancing IoT security. Future work will focus on expanding the dataset to incorporate more diverse and complex malware samples and exploring additional deep learning architectures.