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<p>Traditional Network Intrusion Detection Systems (NIDS) face significant challenges in identifying novel cyberattacks, primarily due to the inherent limitations of signature-based and anomaly-based detection methods. This study proposes an innovative Ensemble Learning (EL) framework that integrates Deep Learning (DL) and Reinforcement Learning (RL) to enhance the capabilities of NIDS. The framework utilizes DL models, specifically convolutional neural networks (CNNs), for effective pattern recognition in network traffic, while incorporating RL agents, particularly Deep Q-Networks (DQNs), to facilitate adaptive threat detection. A key contribution of this work is the implementation of a stacking ensemble technique employing a metaclassifier to combine the outputs of the DL and RL models, thereby improving detection accuracy and reducing false-positive rates. The framework is evaluated using the CIC-IDS2017 and CSE-CIC-IDS2018 datasets. Experimental results show that the EL-based NIDS outperforms individual DL and RL models, achieving an <i>F</i>1 score of 0.95, a notable improvement over baseline approaches. This research presents a practical solution to develop resilient NIDS capable of adapting to emerging cyber threats, thereby strengthening network security and mitigating the impact of cybercrime.</p>

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Resilient cybersecurity: ensemble deep learning and reinforcement learning for Next-Gen IDS

  • 期刊论文

  • 发表日期：

    2026年1月5日

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

摘要

Traditional Network Intrusion Detection Systems (NIDS) face significant challenges in identifying novel cyberattacks, primarily due to the inherent limitations of signature-based and anomaly-based detection methods. This study proposes an innovative Ensemble Learning (EL) framework that integrates Deep Learning (DL) and Reinforcement Learning (RL) to enhance the capabilities of NIDS. The framework utilizes DL models, specifically convolutional neural networks (CNNs), for effective pattern recognition in network traffic, while incorporating RL agents, particularly Deep Q-Networks (DQNs), to facilitate adaptive threat detection. A key contribution of this work is the implementation of a stacking ensemble technique employing a metaclassifier to combine the outputs of the DL and RL models, thereby improving detection accuracy and reducing false-positive rates. The framework is evaluated using the CIC-IDS2017 and CSE-CIC-IDS2018 datasets. Experimental results show that the EL-based NIDS outperforms individual DL and RL models, achieving an F1 score of 0.95, a notable improvement over baseline approaches. This research presents a practical solution to develop resilient NIDS capable of adapting to emerging cyber threats, thereby strengthening network security and mitigating the impact of cybercrime.