Deep Feature Mapping and Ensemble Learning for Advanced IoT Malware Detection and Classification

Abstract

Introduction20.4 With the exponential growth of Internet of Things (IoT) devices, security threats have become a major concern. Traditional malware detection techniques struggle to keep up with the ever-evolving attack landscape due to their reliance on predefined signatures and static rule-based detection. This paper explores the use of deep learning-based feature mapping combined with ensemble learning techniques to enhance IoT malware detection and classification. The proposed approach leverages convolutional neural networks (CNNs) for automatic feature extraction and ensemble models to improve classification accuracy while mitigating overfitting issues. Extensive experiments conducted on benchmark datasets demonstrate the superiority of our approach over traditional methods in terms of detection accuracy, false-positive rates, and computational efficiency. The results indicate that integrating deep learning and ensemble learning methods can significantly enhance the ability to detect and classify malicious IoT activities, making IoT environments more secure against evolving cyber threats.