Enhancing IoT Malware Detection through Adaptive Model Parallelism and Resource Optimization

• URL: http://arxiv.org/abs/2404.08808v1
• Date: Fri, 12 Apr 2024 20:51:25 GMT
• Title: Enhancing IoT Malware Detection through Adaptive Model Parallelism and Resource Optimization
• Authors: Sreenitha Kasarapu, Sanket Shukla, Sai Manoj Pudukotai Dinakarrao,
• Abstract summary: This study introduces a novel approach to malware detection tailored for IoT devices. Based on resource availability, ongoing workload, and communication costs, the malware detection task is dynamically allocated either on-device or offloaded to neighboring IoT nodes. Experimental results demonstrate that this proposed technique achieves a significant speedup of 9.8 x compared to on-device inference.
• Score: 0.6856683556201506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The widespread integration of IoT devices has greatly improved connectivity and computational capabilities, facilitating seamless communication across networks. Despite their global deployment, IoT devices are frequently targeted for security breaches due to inherent vulnerabilities. Among these threats, malware poses a significant risk to IoT devices. The lack of built-in security features and limited resources present challenges for implementing effective malware detection techniques on IoT devices. Moreover, existing methods assume access to all device resources for malware detection, which is often not feasible for IoT devices deployed in critical real-world scenarios. To overcome this challenge, this study introduces a novel approach to malware detection tailored for IoT devices, leveraging resource and workload awareness inspired by model parallelism. Initially, the device assesses available resources for malware detection using a lightweight regression model. Based on resource availability, ongoing workload, and communication costs, the malware detection task is dynamically allocated either on-device or offloaded to neighboring IoT nodes with sufficient resources. To uphold data integrity and user privacy, instead of transferring the entire malware detection task, the classifier is divided and distributed across multiple nodes, then integrated at the parent node for detection. Experimental results demonstrate that this proposed technique achieves a significant speedup of 9.8 x compared to on-device inference, while maintaining a high malware detection accuracy of 96.7%.

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