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The Top Ten Artificial Intelligence-Deep Neural Networks for IoT Intrusion Detection System

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Abstract

Despite the fact that there are numerous methods for detecting IoT intrusions, this research explorations conducted the implementation of the Top 10 Artificial Intelligence—Deep neural networks may be advantageous for unsupervised as well as supervised learning concerning IoT network traffic data. It shows a thorough comparison study to detect IoT intrusions on intelligent embedded devices which are essential to detect such intrusions using the most recent dataset IoT-23. Although several solutions are being developed to secure IoT networks, development might still be required. The use of different deep learning techniques may enhance IoT security. To enhance the security execution of IoT network traffic, the top 10 deep-learning approaches were investigated using the realistic IoT-23 dataset. For recognizing five different IoT attack classes—DoS (Denial of Service), Mirai, Scan, Normal records, and MITM-ARP (Man in the Middle attack)—we developed a variety of neural network models. In deep-learning neural network models, a "softmax" function of multiclass classification may be used to identify these assaults. NumPy, Pandas, Scikit-learn, Scipy, TensorFlow 2.2, Seaborn, and Matplotlib were only some of the programs used in the Anaconda3 environment for this study. Healthcare, banking, finance, scientific research, and corporate organizations, as well as ideas like the Internet of Things, are just some of the many fields that have embraced the utilization of AI-deep learning models. We discovered that the best deep-learning algorithms are capable of minimizing function loss, improving accuracy, as well as reducing execution time for developing that particular model. By using cutting-edge technology like deep learning neural networks as well as artificial intelligence, it makes a significant contribution to the identification of IoT anomalies. As a result, it will be effective to reduce attacks on IoT organizations. CNN (Convolutional neural networks), GANs (generative adversarial networks), and multilayer perceptron provide the best accuracy scores of 0.996317, 0.995829, and 0.996157 among the top 10 neural networks, respectively, with the smallest loss function and the shortest execution times. This paper helped to fully understand the peculiarities of IoT anomaly detection. To help you better understand various neural network models and IoT anomaly detection, this study analysis shows the Top 10 AI-deep learning model implementations.

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Acknowledgements

I appreciate Dr. T. Prem Jacob, my co-author, for his continual support and encouragement as well as for reviewing the final edit of this work. Additionally, I'd like to thank the doctorate committee members of mine, respectable review members, and also the honorable EiC, 'Ramjee Prasad', 'Tim Kersjes', 'Iratxe Puebla', who have supported my research and also helped me to promote my research publications.

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  1. Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India

    V. Kanimozhi & T. Prem Jacob

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  1. V. Kanimozhi
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Correspondence to V. Kanimozhi.

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Kanimozhi, V., Jacob, T.P. The Top Ten Artificial Intelligence-Deep Neural Networks for IoT Intrusion Detection System. Wireless Pers Commun 129, 1451–1470 (2023). https://doi.org/10.1007/s11277-023-10198-6

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