4.8 Article

A Deep Subdomain Adaptation Network With Attention Mechanism for Malware Variant Traffic Identification at an IoT Edge Gateway

Journal

IEEE INTERNET OF THINGS JOURNAL
Volume 10, Issue 5, Pages 3814-3826

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2022.3160755

Keywords

Malware; Logic gates; Internet of Things; Transfer learning; Training; Feature extraction; Image edge detection; Attention mechanism; edge security; Internet of Things (IoT); malware variant traffic identification; subdomain adaptation; transfer learning

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This article proposes a deep subdomain adaptation network with attention mechanism (DSAN-AT) to accurately and efficiently identify malware variant traffic at an IoT edge gateway. DSAN-AT utilizes local maximum mean discrepancy (LMMD) to align the traffic feature distributions of subdomains in the source and target domains. It also exploits channel and spatial attention mechanisms to accelerate learning traffic features between different subdomains to save precious computing resources at the IoT edge gateway.
The prevailing of malware variants in ubiquitous Internet of Things (IoT) devices causes enormous losses. Accurate and timely identification of malware variant traffic at an IoT edge gateway can effectively reduce the loss. TransNet, the state-of-the-art technology for malware variant traffic detection, considers only global domain adaptation and ignores the alignment of distributions between different subdomains, which fails to capture the fine-grained information of classification targets. Besides, TransNet converges very slowly, which may use up precious resources in IoT devices. This article proposes a deep subdomain adaptation network with attention mechanism (DSAN-AT) to accurately and efficiently identify malware variant traffic at an IoT edge gateway. DSAN-AT utilizes local maximum mean discrepancy (LMMD) to align the traffic feature distributions of subdomains in the source and target domains. It also exploits channel and spatial attention mechanisms to accelerate learning traffic features between different subdomains to save precious computing resources at the IoT edge gateway. Our experimental study demonstrates that DSAN-AT achieves an average accuracy of 97.15% (96.37% for TransNet) and converges fast without using a large target domain training data set. DSAN-AT has strong practicality for identifying malware variant traffic at an edge IoT gateway.

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