MalwareNet: an intelligent malware detection and classification using advanced extreme leaning machine in edge computing environment
P. Shailaja, Thanveer Jahan, Karramreddy Sharmila, P. Bharat Siva Varma, Swetha Arra, Pala Mahesh Kumar · Egyptian Informatics Journal · 2025
Malware continues to wreak havoc on global digital ecosystems , with companies facing an average financial loss of $4.35 million per data breach in recent years. At the same time, individual users suffer from identity theft, affecting over 1.1 billion personal records annually. Existing malware detection systems often struggle with high latency in centralized cloud environments and fail to generalize across diverse malware variants generated by edge devices. To address these challenges, this work introduces MalwareNet, a novel multiclass malware detection network designed specifically for edge computing environments. MalwareNet innovatively processes data directly on edge devices, enabling real-time detection and classification with minimal latency and enhanced data privacy. The system employs a robust preprocessing pipeline to clean raw data, followed by Independent Component Analysis (ICA) to extract discriminative features while reducing dataset dimensionality. A Hybrid Wrapper-Filter (HWF) feature selection method optimizes feature subsets by integrating wrapper and filter techniques, ensuring compatibility with the chosen machine-learning classifier to maximize classification accuracy . The Extreme Learning Machine (ELM), selected for its rapid training and strong generalization, classifies malware into distinct categories, effectively identifying threats in edge settings. By combining edge-based processing, advanced feature engineering, and efficient classification, MalwareNet offers a scalable and reliable solution, significantly advancing malware detection capabilities for resource-constrained environments and providing a foundation for future adaptive security systems. Experimental evaluations on a large-scale malware dataset demonstrate the effectiveness of the proposed approach with an accuracy of 99.7 %, and F-measure of 99.55 %. The system also achieves high Jaccard index with an increment of 2.63 % in detecting and classifying malware, providing reliable security measures in edge computing environments.