Optimized Cascading Long Short-Term Memory model with Latin Sampling Satin Bowerbird Optimization Algorithm for Intrusion Detection in Internet of Things

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 7

Year of Publication : 2025

Authors : Naveen Thimmahanumaiah Hosur, Vasantha Kumara Mahadevachar, Venkatesh Prasad BS, Thirthe Gowda MT

10.14445/23488379/IJEEE-V12I7P120

How to Cite?

Naveen Thimmahanumaiah Hosur, Vasantha Kumara Mahadevachar, Venkatesh Prasad BS, Thirthe Gowda MT, "Optimized Cascading Long Short-Term Memory model with Latin Sampling Satin Bowerbird Optimization Algorithm for Intrusion Detection in Internet of Things," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 7, pp. 274-286, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I7P120

Abstract:

The rapid development of Internet of Things (IoT) networks has significantly increased their vulnerability to cyberattacks, so it is essential to develop effective Intrusion Detection Systems (IDS). Traditional algorithms often struggle with a high false positive rate and scalability issues in dynamic IoT environments. For addressing these challenges, this article proposed Optimized Cascading Long Short-Term Memory with Latin Sampling Satin Bowerbird Optimization (OCLSTM-LSBO) algorithm to effectively identify intrusions. The deep cascading LSTM framework captures the deep temporal dependencies in network traffic and improves the identification of difficult intrusion patterns in IoT networks. Then, employed the LSBO algorithm to fine-tune the hyperparameters of the LSTM model, which improves classification accuracy and enhances the generalization ability of the model. In the pre-processing phase, the Min-Max normalization technique is used to normalise the features in a uniform range. The OCLSTM-LSBO algorithm obtained the highest accuracy of 98.97% using the CICIoT2023 dataset and 95.62% using ToNIoT dataset for multiclass classification when compared to existing algorithms like Federated Multi Layered Deep-Learning (Fed-MLDL).

Keywords:

Internet of Things, Intrusion Detection System, Latin sampling satin bowerbird optimization, Multiclass classification, Optimized Cascading Long Short-Term Memory model.

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