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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 05

Published on: May 2026

COMPARATIVE ANALYSIS OF OPTIMIZATION STRATEGIES FOR NEURAL NETWORK-BASED NETWORK INTRUSION DETECTION ON THE UNSW-NB15 DATASET

Anghan Priyank Aryan Manhas Apoorva Kumari Chandrika Lamani

Department of Computer Science and Engineering RV Institute of Technology and Management
JP Nagar–560076 Bengaluru
DOI:https://doi.org/10.55041/ijcope.v2i5.261

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Abstract

Network Intrusion Detection Systems (NIDS) are a critical component of modern cybersecurity infrastructure. This paper extends the Neural Network-based NIDS framework proposed by Subba (2019) by introducing two novel contributions: (1) the Adam optimizer as an alternative to Stochastic Gradient Descent (SGD) for training the MLP classifier, and (2) a Random Forest classifier as a strong ensemble baseline. All three models are evaluated on the contemporary UNSW-NB15 binary classification dataset. Experimental results show that the Adam-optimized neural network (Accuracy: 96.58%, AUC: 1.00) outperforms the SGD baseline (Accuracy: 96.19%, AUC: 0.99), and the Random Forest achieves the highest overall performance (Accuracy: 97.59%, AUC: 1.00). These findings confirm the benefit of adaptive optimization and ensemble methods for real-time network anomaly detection.

Keywords: NIDS, Neural Network, Random Forest, Adam Optimizer, SGD, UNSW-NB15, Intrusion Detection, Anomaly Detection

How to Cite this Paper

Priyank, A., Manhas, A., Kumari, A. & Lamani, C. (2026). Comparative Analysis of Optimization Strategies for Neural Network-Based Network Intrusion Detection on the UNSW-NB15 Dataset. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.261

Priyank, Anghan, et al.. "Comparative Analysis of Optimization Strategies for Neural Network-Based Network Intrusion Detection on the UNSW-NB15 Dataset." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.261.

Priyank, Anghan,Aryan Manhas,Apoorva Kumari, and Chandrika Lamani. "Comparative Analysis of Optimization Strategies for Neural Network-Based Network Intrusion Detection on the UNSW-NB15 Dataset." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.261.

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References

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  • •Published on: May 08 2026
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