An efficient cloud-integrated distributed deep neural network framework for IoT malware classification

• Published in: Future generation computer systems Vol. 157; pp. 603 - 617
• Main Authors: Babaei Mosleh, Mohammad Reza, Sharifian, Saeed
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2024
• Subjects: Cloud computing; CNN; Cyber security; Edge computing; IoT; Malware classification; Cloud computing; CNN; Cyber security; Malware classification; Edge computing; IoT
• DOI: 10.1016/j.future.2024.03.051
• ISSN: 0167-739X

The proliferation of interconnected devices in the Internet of Things (IoT) landscape has introduced significant security concerns. With the integration of android devices, the potential for attackers to exploit vulnerabilities becomes a crucial issue. Timely detection of malware attacks has emerged as a critical challenge, especially in industrial IoT (IIoT), to prevent permanent damage. The slow inference speed of the current algorithms causes limited performance and a significant delay in the detection and mitigation of malware, especially in large-scale environments such as IIoT. High resource utilization can limit the size of the model (number of parameters), which could be implemented in embedded devices, which causes lower accuracy of the model. Hence, we proposed a HierarchicalCloudDNN, a distributed malware detection framework that used modified state-of-the-art deep learning algorithms in a boosting ensemble architecture. The proposed method could scale efficiently from IIoT devices up to the edge and cloud to speed up malware detection, simultaneously reduce resource utilization, and maintain a controllable level of accuracy compared to rival methods in malware detection. The objective is to minimize both run-time and resource consumption while maintaining performance characteristics. The performance analysis of the HierarchicalCloudDNN method was evaluated using standard performance measures on the frequently used BIG 2015 dataset. Based on evaluations, the proposed approach has demonstrated an impressive accuracy rate of 98.90%, outperforming many state-of-the-art models in performance, response time, and resource utilization. These findings suggest a notable decrease in both false positives and false negatives while maintaining accuracy. The proposed distributed malware analysis framework is scalable, robust, and helpful for the timely detection of malicious activity, especially in geographically distributed environments such as the IIoT. •We proposed a distributed malware detection framework especially in geographically distributed environments such as the IIoT that used modified state-of-the-art deep learning algorithms in a boosting ensemble architecture.•The method could scale efficiently from IIoT devices up to the edge and cloud to speed up malware detection, and simultaneously reduce resource utilization and maintain a controllable level of accuracy for malware detection.•Based on evaluations and implementation of our framework on an embedded device (Nvidia Jetson Nano) the proposed approach has demonstrated an impressive accuracy rate of 98.90%, outperforming many state-of-the-art models in performance, response time, and resource utilization.