Abstract
Securing the Internet of Things-based environment is a top priority for consumers, businesses, and governments. There are billions of devices connecting and sharing data; an attack might cost billions of dollars. As a result, it’s important to protect the IoT network from external and internal threats. There is no way to guarantee that all vulnerabilities will be fixed with a single solution or that no additional flaws will be discovered. This paper proposes a deep learning-based solution to detect network intrusion in an IoT network to better prepare for network attacks. The proposed solution achieves the optimal tradeoff between accuracy and model weightage and ensures it is well-suited for resource-constrained IoT devices. The proposed solution uses a reduced data set for training produced by incremental PCA with LSTM, GRU, and BiLSTM. The proposed solution reduced the training time significantly while retaining the accuracy of 98.17% with GRU, 98.12% with LSTM, and 98.23% with BiLSTM, and the results show that the proposed model has better performance in training the model for detecting network intrusion in an IoT network.
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Chaurasia, A., Mishra, A., Rao, U.P., Kumar, A. (2024). Deep Learning-Based Solution for Intrusion Detection in the Internet of Things. In: Muthalagu, R., P S, T., Pawar, P.M., R, E., Prasad, N.R., Fiorentino, M. (eds) Computational Intelligence and Network Systems. CINS 2023. Communications in Computer and Information Science, vol 1978. Springer, Cham. https://doi.org/10.1007/978-3-031-48984-6_7
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