Enhancing network intrusion detection with stacked deep and reinforcement learning models.

Kalpani, Nethma; Rodrigo, Nureka; Seneviratne, Dilmi; Ariyadasa, Subhash; Senanayake, Janaka

Abstract

This study investigates the effectiveness of Ensemble Learning (EL) techniques by integrating reproducible Deep Learning (DL) and Reinforcement Learning (RL) models to enhance network intrusion detection. Through a systematic review of the literature, the most effective DL and RL models from 2020 to 2024 were identified based on their F1 scores and reproducibility, focusing on recent advancements in network intrusion detection. A structured normalisation and evaluation process allowed for an objective comparison of model performances. The best performing DL and RL models were subsequently integrated using a stacking ensemble technique, chosen for its ability to combine the complementary strengths of the DL and RL models. Experimental validation in a benchmark dataset confirmed the high accuracy and robust detection capabilities of the model, outperforming the individual DL and RL models to detect network intrusions in multiple classes. This research demonstrates the potential of ensemble methods for advancing Intrusion Detection Systems (IDSs), offering a scalable and effective solution for dynamic cybersecurity environments.

Citation

KALPANI, N., RODRIGO, N., SENEVIRATNE, D., ARIYADASA, S. and SENANAYAKE, J. 2025. Enhancing network intrusion detection with stacked deep and reinforcement learning models. In Proceedings of the 8th International research conference on Smart computing and systems Engineering 2025 (SCSE 2025), 3 April 2025, Colombo, Sri Lanka. Piscataway: IEEE [online], pages 1-7. Available from: https://doi.org/10.1109/SCSE65633.2025.11031023

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 8th International research conference on Smart computing and systems engineering 2025 (SCSE 2025)
• Start Date: Apr 3, 2025
• Acceptance Date: Feb 7, 2025
• Online Publication Date: Apr 3, 2025
• Publication Date: Apr 3, 2025
• Deposit Date: Jun 19, 2025
• Publicly Available Date: Jul 8, 2025
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Series ISSN: 2613-8662
• DOI: https://doi.org/10.1109/SCSE65633.2025.11031023
• Keywords: Deep learning; Ensemble learning; Machine learning; Network intrusion detection; Reinforcement learning
• Public URL: https://rgu-repository.worktribe.com/output/2885846