Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms

Bo Cui, Yachao Chai, Zhen Yang and Keqin Li ()
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Bo Cui: College of Computer Science, Inner Mongolia University, Hohhot 010021, China
Yachao Chai: College of Computer Science, Inner Mongolia University, Hohhot 010021, China
Zhen Yang: College of Computer Science, Inner Mongolia University, Hohhot 010021, China
Keqin Li: Department of Computer Science, State University of New York, New Paltz, NY 12561, USA

Future Internet, 2024, vol. 16, issue 7, 1-15

Abstract: Connected devices in IoT systems usually have low computing and storage capacity and lack uniform standards and protocols, making them easy targets for cyberattacks. Implementing security measures like cryptographic authentication, access control, and firewalls for IoT devices is insufficient to fully address the inherent vulnerabilities and potential cyberattacks within the IoT environment. To improve the defensive capabilities of IoT systems, some research has focused on using deep learning techniques to provide new solutions for intrusion detection systems. However, some existing deep learning-based intrusion detection methods suffer from inadequate feature extraction and insufficient model generalization capability. To address the shortcomings of existing detection methods, we propose an intrusion detection model based on temporal convolutional residual modules. An attention mechanism is introduced to assess feature scores and enhance the model’s ability to concentrate on critical features, thereby boosting its detection performance. We conducted extensive experiments on the ToN_IoT dataset and the UNSW-NB15 dataset, and the proposed model achieves accuracies of 99.55% and 89.23% on the ToN_IoT and UNSW-NB15 datasets, respectively, with improvements of 0.14% and 15.3% compared with the current state-of-the-art models. These results demonstrate the superior detection performance of the proposed model.

Keywords: IoT; cyber attacks; intrusion detection; deep learning; attention mechanism (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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