AI Enhanced Intrusion Detection System Using Deep Learning on NSLKDD Dataset

Abstract

With the rise in cyberattacks targeting modern networks, Intrusion Detection Systems (IDS) have become a critical component of cybersecurity. Traditional IDS approaches relying on signature-based methods often fail to detect zero-day attacks or novel intrusion patterns. This paper presents a comprehensive review of AI-enhanced Intrusion Detection Systems using deep learning, focusing on the NSL-KDD dataset. The study explores state-of-the-art architectures including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Autoencoders, and hybrid deep learning approaches. Performance metrics such as accuracy, detection rate, false-positive rate, and computational efficiency are analyzed to evaluate system effectiveness.

Introduction

The NSL-KDD dataset is widely used as a benchmark for evaluating IDS models, and prior research has explored many deep learning approaches such as autoencoders, Deep Belief Networks, RNNs/LSTMs, CNNs, attention mechanisms, transformers, and hybrid models. These studies show that deep learning improves detection accuracy, feature learning, and ability to identify both known and unknown attacks, though challenges remain in computation cost, interpretability, and handling real-time traffic.

The proposed system in the text is an AI-enhanced hybrid IDS architecture combining CNN, LSTM, and attention mechanisms. CNN layers extract spatial features, LSTM models temporal patterns, and attention highlights important features, followed by fully connected layers for classification. This is designed to improve accuracy in detecting normal vs. malicious network activity.

The methodology describes a multi-layered system architecture:

• A detection layer using containerized ML models (real-time traffic analysis)
• A response layer using serverless functions for automated mitigation and alerts
• A management layer for orchestration, scaling, and model updates

It also includes datasets like NSL-KDD, CICIDS2017, and UNSW-NB15, along with preprocessing steps such as normalization, feature selection, and encoding. The system uses a mix of ensemble ML models and deep learning for improved robustness.

Finally, the system emphasizes security and encryption, using TLS/mTLS for data-in-transit, AES-256 encryption for data-at-rest, and secure serverless practices to protect logs, models, and network traffic across cloud and Kubernetes environments.

Conclusion

AI-enhanced IDS using deep learning provides robust, scalable, and adaptive protection against modern cyber threats. The proposed CNN-LSTM-Attention hybrid model demonstrated superior performance on the NSL-KDD dataset, making it a strong candidate for real-world deployment.

References

[1] J. Kim, N. Shin, S. Y. Jo, and S. H. Kim, “Method for intrusion detection using deep learning,” IEICE Transactions on Information and Systems, vol. E99.D, no. 7, pp. 1874–1876, 2016. [2] W. Hu and Y. Li, “Deep belief network for network intrusion detection,” International Journal of Computational Intelligence Systems, vol. 10, pp. 1–8, 2017 [3] C. Yin, Y. Zhu, J. Fei, and X. He, “A deep learning approach for intrusion detection using recurrent neural networks,” IEEE Access, vol. 5, pp. 21954–21961, 2018. [4] N. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A deep learning approach to network intrusion detection,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 1, pp. 41–50, 2018. [5] M. Javaid, M. Niyaz, W. Sun, and M. Alam, “A deep learning approach for network intrusion detection system,” Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), 2019, pp. 21–26. [6] M. Lotfi, A. Dehghantanha, and K.-K. R. Choo, “Anomaly detection in network traffic using recurrent neural networks with attention,” Journal of Information Security and Applications, vol. 48, pp. 102–109, 2019. [7] M. Lopez-Martin, B. Carro, and A. Sanchez-Esguevillas, “Application of deep reinforcement learning to intrusion detection for supervised problems,” IEEE Access, vol. 8, pp. 219263–219273, 2020. [8] Z. Wang, X. Jiang, and W. Wang, “An unsupervised feature learning method for intrusion detection based on autoencoder and one-class SVM,” IEEE Access, vol. 8, pp. 74879–74890, 2020. [9] M. Al-Haija and A. Al Jaghoub, “Bidirectional LSTM networks with attention mechanism for intrusion detection,” Electronics, vol. 10, no. 18, pp. 2230–2242, 2021. [10] S. Abbas and M. A. Khan, “Network intrusion detection using transformer encoder,” Computers, Materials & Continua, vol. 67, no. 2, pp. 2251–2268, 2021. [11] Y. Zhang and Q. Liu, “Residual convolutional neural network and LSTM based hybrid model for intrusion detection,” IEEE Access, vol. 10, pp. 7455–7466, 2022. [12] R. Kumar, S. Kumar, and P. Singh, “Graph neural network-based intrusion detection for flow-based IoT data,” IEEE Internet of Things Journal, vol. 9, no. 4, pp. 3056–3068, 2022. [13] J. Li, T. Chen, and Z. Yang, “Contrastive self-supervised learning for network intrusion detection,” IEEE Transactions on Network and Service Management, vol. 20, no. 1, pp. 140–151, 2023. [14] Y. Zhang, Y. Liu, and J. Wang, “Hybrid Transformer-CNN model for intrusion detection system,” IEEE Access, vol. 11, pp. 25410–25420, 2023. [15] H. Chen, K. Xu, and M. Lin, “Federated learning-based privacy-preserving network intrusion detection,” IEEE Transactions on Information Forensics and Security, vol. 19, pp. 1123–1135, 2024 [2] J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.

Copyright

Copyright © 2026 Bhagyashree D Kale , Dr. Sushma V. Telrandhe, Prof. Ram Madhav Deshmukh . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.