Published November 30, 2025 | Version CC-BY-NC-ND 4.0
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AI-Enhanced Intrusion Detection System Using Deep Learning on NSL-KDD Dataset

Authors/Creators

  • 1. Department of Computer Science and Engineering, Nagpur University, Pune (Maharashtra), India

Contributors

Contact person:

  • 1. Department of Computer Science and Engineering, Nagpur University, Pune (Maharashtra), India
  • 2. Assistant Professor, Department of Computer Science and Engineering, Nagpur University, Pune (Maharashtra), India

Description

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 (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), 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.

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Additional details

Identifiers

DOI
10.35940/ijsce.F3698.15051125
EISSN
2231-2307

Dates

Accepted
2025-11-15
Manuscript Received on 24 October 2025 | Revised Manuscript Received on 03 November 2025 | Manuscript Accepted on 15 November 2025 | Manuscript published on 30 November 2025

References

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  • 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, 2017. DOI: http://doi.org/10.1109/ACCESS.2017.2762418
  • 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, Feb. 2018. DOI: http://doi.org/10.1109/TETCI.2017.2772792
  • A. Javaid, Q. Niyaz, W. Sun, and M. Alam, "A Deep Learning Approach for Network Intrusion Detection System," in Proc. 9th EAI International Conf. on Bio-inspired Information and Communications Technologies (BICT), 2016. DOI: http://doi.org/10.4108/eai.3-12-2015.2262516
  • M. A. Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, "Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study," J. Inf. Secur. Appl., vol. 50, 2020. DOI: 10.1016/j.jisa.2019.102419.
  • M. Umer, S. Sadiq, H. Karamti et al., "Deep Learning-Based Intrusion Detection Methods in Cyber-Physical Systems: Challenges and Future Trends," Electronics, vol. 11, no. 20, article 3326, 2022. DOI: http://doi.org/10.3390/electronics11203326
  • A. Binbusayyis, "Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM," Soft Comput., 2021. DOI: http://doi.org/10.1007/s10489-021-02205-9
  • I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "Toward generating a new intrusion detection dataset and intrusion traffic characterisation," in Proc. International Conference on Information Systems Security and Privacy (ICISSP / ICISSP 2018), 2018, pp. 108–116. DOI: http://doi.org/10.5220/0006639801080116 (CIC-IDS / CICIDS2017 dataset creators — important when discussing datasets.)
  • N. Moustafa and J. Slay, "UNSW-NB15: A comprehensive data set for network intrusion detection systems," Proc. Military Communications and Information Systems Conference (MilCIS), 2015. DOI: http://doi.org/10.1109/MilCIS.2015.7348942 (UNSW-NB15 dataset — widely used as a modern benchmark.)
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  • J. Lee and K. Park, "AE-CGAN Model-based High Performance Network Intrusion Detection System," Applied Sciences, vol. 9, no. 20, article 4221, 2019. DOI: http://doi.org/10.3390/app9204221
  • S. Gamage, A. Perera, S. Suganya et al., "Deep learning methods in network intrusion detection: taxonomy, challenges and future directions," J. Netw. Comput. Appl., 2020. DOI: http://doi.org/10.1016/j.jnca.2020.102564 (survey/taxonomy useful for literature review)
  • L. Binbusayyis (A. Binbusayyis is also listed above) — another strongly cited unsupervised IDS paper is: A. Binbusayyis, "Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM," Applied Intelligence (or Soft Comput. entry). DOI given in item 6. (Kept here for context/state-of-the-art; see item 6.)
  • W. Lee, S. Stolfo, and K. Mok, "A data mining framework for building intrusion detection models," in Proc. IEEE Symposium on Security and Privacy, 1999 — classic foundational paper; while old, it's frequently cited. DOI (conference format may not have a CrossRef DOI; cite as conference proc.). (Include for historical grounding — no DOI required in many referencing styles.)
  • R. Almuhanna, "A deep learning/machine learning approach for anomaly-based network intrusion detection — a comparative study," IEEE Access / Springer chapter (2020 / 2021) — see DOI http://doi.org/10.1201/9780429270567-8 for the empirical assessment chapter "Deep Learning for Network Intrusion Detection: An Empirical Assessment." DOI: http://doi.org/10.1201/9780429270567-8
  • S. Aldhaheri, B. A. Alzahrani, and S. Alshamrani, "SGAN-IDS: Self-Attention-Based Generative Adversarial Network for Synthetic Intrusion Generation and Detection," Sensors, 2023. DOI: http://doi.org/10.3390/s23187796