Skip to main content
SpringerLink
Log in

Quantum particle Swarm optimized extreme learning machine for intrusion detection

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Ensuring a secure online environment hinges on the timely detection of network attacks. Nevertheless, existing detection methods often grapple with the delicate balance between speed and accuracy. In this paper, we introduce a novel intrusion detection algorithm that marries quantum particle swarm optimization with an extreme learning machine (QPSO-ELM). Firstly, we present a feature selection algorithm grounded in partitioned gains to distill vital features from data samples, thereby diminishing feature count to amplify both model training speed and accuracy. Subsequently, we unveil an intrusion detection scheme underpinned by QPSO-ELM, capable of achieving exceptional levels of training and detection speed, all while maintaining high accuracy. Finally, we fine-tune the trained model using the proposed hidden layer node selection algorithm, reducing the detection model size without compromising detection accuracy, thus further elevating its speed. The experiment results indicate that compared to the current baseline, our proposed intrusion detection scheme achieves the best results in terms of accuracy, precision, recall, and detection latency. Furthermore, the ablation experiment results demonstrate the effectiveness of our proposed method in improving both detection speed and detection accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Xu S, Zhong J, Wang L, He D, Zhang S, Shao W (2023) A privacy-preserving and efficient data sharing scheme with trust authentication based on blockchain for mhealth. Connect Sci 35(1):1. https://doi.org/10.1080/09540091.2023.2186316

    Article  ADS  Google Scholar 

  2. Rathore MM, Ahmad A, Paul A (2016) Real time intrusion detection system for ultra-high-speed big data environments. J Supercomput 72(9):3489–3510. https://doi.org/10.1007/S11227-015-1615-5

    Article  Google Scholar 

  3. Kumar G (2020) An improved ensemble approach for effective intrusion detection. J Supercomput 76(1):275–291. https://doi.org/10.1007/S11227-019-03035-W

    Article  Google Scholar 

  4. Sepczuk M (2023) Dynamic web application firewall detection supported by cyber mimic defense approach. J Netw Comput Appl 213:103596. https://doi.org/10.1016/j.jnca.2023.103596

    Article  Google Scholar 

  5. Zhao Y, Pang Y, Ke X, Wang B, Zhu G, Cao M (2023) A metaverse-oriented CP-ABE scheme with cryptographic reverse firewall. Fut Gen Comput Syst 147:195–206. https://doi.org/10.1016/j.future.2023.04.025

    Article  Google Scholar 

  6. Alexander R, Kumar KPM (2023) Hybrid rule based classification of attacks in internet of things (iot) intrusion detection system. In: 2023 7th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, pp 1249–1254

    Chapter  Google Scholar 

  7. Luo F, Yang Z, Zhang Z, Wang Z, Wang B, Wu M (2023) A multi-layer intrusion detection system for some/ip-based in-vehicle network. Sensors 23(9):4376. https://doi.org/10.3390/s23094376

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  8. Uszko K, Kasprzyk M, Natkaniec M, Chołda P (2023) Rule-based system with machine learning support for detecting anomalies in 5g wlans. Electronics 12(11):2355

    Article  Google Scholar 

  9. Devi M, Nandal P, Sehrawat H (2023) A novel rule-based intrusion detection framework for secure wireless sensor networks. PREPRINT (Version 1) available at Research Square. https://doi.org/10.21203/rs.3.rs-2639778/v1

  10. Kumar A, Das TK (2023) Rule-based intrusion detection system using logical analysis of data. In: 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, pp 129–135

    Chapter  Google Scholar 

  11. Hu B, Bi Y, Zhi M, Zhang K, Yan F, Zhang Q, Liu Z (2022) A deep one-class intrusion detection scheme in software-defined industrial networks. IEEE Trans Ind Inf 18(6):4286–4296. https://doi.org/10.1109/TII.2021.3133300

    Article  Google Scholar 

  12. Talukder MA, Hasan KF, Islam MM, Uddin A, Akhter A, Yousuf MA, Alharbi F, Moni MA (2023) A dependable hybrid machine learning model for network intrusion detection. J Inf Secur Appl 72:103405. https://doi.org/10.1016/j.jisa.2022.103405

    Article  Google Scholar 

  13. Sharma B, Sharma L, Lal C, Roy S (2023) Anomaly based network intrusion detection for iot attacks using deep learning technique. Comput Electr Eng 107:108626. https://doi.org/10.1016/j.compeleceng.2023.108626

    Article  Google Scholar 

  14. Chowdhury R, Sen S, Goswami A, Purkait S, Saha B (2023) An implementation of bi-phase network intrusion detection system by using real-time traffic analysis. Expert Syst Appl 224:119831. https://doi.org/10.1016/j.eswa.2023.119831

    Article  Google Scholar 

  15. Debicha I, Bauwens R, Debatty T, Dricot J, Kenaza T, Mees W (2023) TAD: transfer learning-based multi-adversarial detection of evasion attacks against network intrusion detection systems. Fut Gen Comput Syst 138:185–197. https://doi.org/10.1016/j.future.2022.08.011

    Article  Google Scholar 

  16. Tran B, Xue B, Zhang M, Nguyen S (2016) Investigation on particle swarm optimisation for feature selection on high-dimensional data: local search and selection bias. Connect Sci 28(3):270–294. https://doi.org/10.1080/09540091.2016.1185392

    Article  ADS  Google Scholar 

  17. Daweri MSA, Ariffin KAZ, Abdullah S, Senan MFEM (2020) An analysis of the KDD99 and UNSW-NB15 datasets for the intrusion detection system. Symmetry 12(10):1666. https://doi.org/10.3390/SYM12101666

    Article  ADS  Google Scholar 

  18. Shu W, Yan Z, Yu J, Qian W (2023) Information gain-based semi-supervised feature selection for hybrid data. Appl Intell 53(6):7310–7325. https://doi.org/10.1007/S10489-022-03770-3

    Article  Google Scholar 

  19. Sun J, Xu W, Feng B (2005) Adaptive parameter control for quantum-behaved particle swarm optimization on individual level. In: Proceedings of the IEEE International Conference on Systems, man and Cybernetics, Waikoloa, Hawaii, USA, October 10–12, 2005, pp 3049–3054. https://doi.org/10.1109/ICSMC.2005.1571614

  20. Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE symposium on computational intelligence for security and defense applications, CISDA 2009, Ottawa, Canada, July 8–10, 2009, pp 1–6 . https://doi.org/10.1109/CISDA.2009.5356528

  21. Vitorino J, Andrade R, Praça I, Sousa O, Maia E (2021) A comparative analysis of machine learning techniques for iot intrusion detection. In: Aïmeur E, Laurent M, Yaich R, Dupont B, García-Alfaro J (eds) Foundations and Practice of Security—14th International Symposium, FPS 2021, Paris, France, December 7–10, 2021, Revised Selected Papers. Lecture Notes in Computer Science, vol 13291, pp 191–207. https://doi.org/10.1007/978-3-031-08147-7_13

  22. Shilpashree S, Lingareddy S, Bhat N, Kumar G (2019) Decision tree: a machine learning for intrusion detection. Int J Innov Technol Explor Eng (IJITEE) 8(6S4):1126–1130

  23. Ajdani M, Ghaffary HR (2021) Introduced a new method for enhancement of intrusion detection with random forest and PSO algorithm. Secur Priv 4(2):1. https://doi.org/10.1002/SPY2.147

    Article  Google Scholar 

  24. Mokbal FMM, Wang D, Osman M, Yang P, Alsamhi SH (2022) An efficient intrusion detection framework based on embedding feature selection and ensemble learning technique. Int Arab J Inf Technol 19(2):237–248 https://doi.org/10.34028/IAJIT/19/2/11

  25. Ali MH, Fadlizolkipi M, Firdaus A, Khidzir NZ (2018) A hybrid particle swarm optimization—extreme learning machine approach for intrusion detection system. In: 2018 IEEE Student Conference on Research and Development (SCOReD), pp 1–4. https://doi.org/10.1109/SCORED.2018.8711287

Download references

Acknowledgements

This work was funded in part by the Liaoning Provincial Department of Education Research under Grant LJKZ0208, in part by the Scientific Research Foundation for Advanced Talents from Shenyang Aerospace University under Grant 18YB06.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Shenyang Aerospace University, Shenyang, 100136, China

    Han Qi, Xinyu Liu & Changqing Gong

  2. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, 50603, Malaysia

    Abdullah Gani

Authors
  1. Han Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinyu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdullah Gani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Changqing Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinyu Liu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qi, H., Liu, X., Gani, A. et al. Quantum particle Swarm optimized extreme learning machine for intrusion detection. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06022-y

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-024-06022-y

Keywords

Search

Navigation