Abstract
The rapid development of IoT networks emphasises the critical importance of robust security measures. Consequently, anomaly-based intrusion detection systems using machine learning techniques have garnered significant attention due to their ability to detect unseen attacks. This study introduces neural network approaches for network attack detection. We propose supervised learning approaches, combining Artificial Neural Networks (ANN) and 2D Convolutional Neural Networks (2D-CNN) to detect attacks on the IoT-23 dataset. We only consider packets that belong to IPv4 and one of the three protocols: TCP, UDP, or ICMP. The ANN and 2D-CNN have achieved the highest accuracy of 99.71% and 99.34% on the IoT-23 datasets, respectively. Furthermore, by looking at the packet level, the 2D-CNN models show an approximately 40% improvement in feature extraction time compared to ANN models. Our approach offers innovative solutions for network attack detection systems which can be mapped on the latest computing architectures, including CNN accelerators and FPGAs.
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Acknowledgement
This research is supported in part by a grant from Science Foundation Ireland INSIGHT Centre for Data Analytics (Grant number 12/RC/2289-P2) which is co-funded under the European Regional Development Fund. The authors acknowledge the University College Cork (UCC) and Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study. We would like also to acknowledge the support from Qualcomm, Analog Devices, AMD/Xilinx and Dell for various parts of this project.
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Ngo, DM. et al. (2023). Network Attack Detection on IoT Devices Using 2D-CNN Models. In: Dao, NN., Thinh, T.N., Nguyen, N.T. (eds) Intelligence of Things: Technologies and Applications. ICIT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 188. Springer, Cham. https://doi.org/10.1007/978-3-031-46749-3_23
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DOI: https://doi.org/10.1007/978-3-031-46749-3_23
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