Hybrid Ensemble Deep Neural Network for Intrusion Detection (HEDNN ID)
Aluri Brahmareddy, S Meghana, S. Vishwa Kiran · International Journal of Electronics and Communication Engineering · 2025
As network traffic becomes increasingly complex today, the variety of cyber attacks is also increasing, and the need to prevent these attacks in real time is emerging; network intrusion detection systems (NIDSs ) are essential. In contrast, traditional IDS methodologies , incl uding rule based and statistical approaches, often face challenges in maintaining their effectiveness as they struggle to keep pace with the rapid development of Large Scale , dynamic t raffic and the evolving behavior of attackers . Although CNN LSTM and tra nsformer based frameworks improve detection accuracy using state of the art deep learning models, the challenges of addressing spatial temporal dependencies, class imbalance, and scalability remain to be addressed . Such challenges necessitate a robust, fas t, and scalable framework for network level intrusion detection . This research proposes a Hybrid Ensemble Deep Neural Network model termed HEDNN ID to address these limitations. This model explicitly integrates attention mechanisms to focus on significant data, Long Short Term Memory (LSTM) to learn temporal dependencies, Convolutional Neural Networks (CNN) to extract spatial features, and ensemble learning to enhance generalization and resilience. HEDNN ID compared favorably with leading models, achieving 98.68% accuracy, 97.80% precision, 97.50% recall , and 97.65% F1 score on the UNSW NB15 dataset. T he proposed framework effectively addresses the limitations of existing approaches and supports scalability for practical use cases in safe intrusion detection. HEDNN ID can adapt to various attack scenarios and enhance detection reliability, which represe nts a significant step forward in modern cybersecurity. The research provides a foundation for developing impenetrable, scalable IDS frameworks.