| Autor: |
Niandong Liao, Jiayu Guan |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
International Journal of Computational Intelligence Systems, Vol 17, Iss 1, Pp 1-25 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1875-6883 |
| DOI: |
10.1007/s44196-024-00421-y |
| Popis: |
Abstract The Internet of Things (IoT) has been extensively utilized in domains such as smart homes, healthcare, and other industries. With the exponential growth of Internet of Things (IoT) devices, they have become prime targets for malicious cyber-attacks. Effective classification of IoT traffic is, therefore, imperative to enable robust intrusion detection systems. However, IoT traffic data contain intricate spatial relationships and topological information, which traditional methods for traffic identification lack the capability to fully extract features and capture crucial characteristics. We propose a multi-scale convolutional feature fusion network augmented with a Convolutional Block Attention Module (MCF-CBAM) for accurate IoT traffic classification. The network incorporates three critical innovations: (1) Parallel convolution extracts multi-scale spatial features from traffic data. The 1 × 1 convolution operation reduces the amount of parameters and calculations of the network, thereby improving work efficiency. (2) The attention module suppresses less informative features while highlighting the most discriminative ones, enabling focused learning on decisive features. (3) Cross-scale connections with channel jumps reuse features from prior layers to enhance generalization. We evaluate the method extensively on three widely adopted public datasets. Quantitative results demonstrate MCF-CBAM establishes new state-of-the-art performance benchmarks for IoT traffic classification, surpassing existing methods by a significant margin. Qualitative visualizations of the learned attention weights provide intuitive insights into how the network automatically discovers the most decisive spatial features for identification. With its strong empirical performance and interpretable attention mechanisms, this work presents a promising deep learning solution to augment real-world IoT intrusion detection systems against growing cybersecurity threats. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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