OHNN: An Optimized Hybrid Neural Network for Intrusion Detection Systems Using TLBO and CTO

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 9

Year of Publication : 2025

Authors : Shourya Shukla, Ajay Singh Raghuvanshi, Saikat Majumder

10.14445/23488549/IJECE-V12I9P114

How to Cite?

Shourya Shukla, Ajay Singh Raghuvanshi, Saikat Majumder, "OHNN: An Optimized Hybrid Neural Network for Intrusion Detection Systems Using TLBO and CTO," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 9, pp. 162-177, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I9P114

Abstract:

In recent years, cybersecurity has become a hot topic for exploration among researchers. The anomaly-based Detection methods have been widely used for early detection and mitigation of Intrusions. As the hardware is evolving, there is a need for exploration of new features. This is made possible through novel deep learning models. In this paper, an automated construction of various hybrid deep learning models is performed through a twin optimization algorithm. The Teaching Learning Based Optimization and Class Topper Optimization are used to generate new deep learning structures. The model parameters, like the Number of Hidden layers, the Optimizer and the Learning Rate, are controlled by TLBO. CTO was used to select different internal parameters such as Types of layers, Number of nodes per layer, the activation function and initializers. The proposed model was used to explore several deep learning models to train and test on the NSL-KDD dataset. Binary and Multiclass classifiers were explored for different neural network architectures based on TLBO and CTO parameters. The explored models showed comparable accuracy with respect to existing detection models.

Keywords:

Teaching Learning Based Optimization, Class Topper Optimization, Hybrid Neural Network, Intrusion Detection System, Optimization.

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