[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

High-precision intrusion detection for cybersecurity communications based on multi-scale convolutional neural networks

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

Abstract

This study developed an advanced network intrusion detection system based on an improved multi-scale convolutional neural network architecture aimed at enhancing the accuracy of detecting network threats. By precisely capturing data features at different scales, this system significantly improves the model’s ability to analyze complex network behaviors. The proposed system incorporates a novel data preprocessing method combining SMOTE and ENN techniques to address the class imbalance in datasets while resolving the overlap issue of minority and majority class samples present in the SMOTE algorithm. It also utilizes a novel feature selection approach combining Information Gain, Random Forest feature importance scoring, and Recursive Feature Elimination to optimize model performance and reduce computational load. Experiments conducted on public datasets CICIDS2017, KDDCUP99, and UNSW-NB15. The experimental results demonstrate that intrusion detection based on a multi-scale convolutional neural network exhibits high detection accuracy. Specifically, the accuracy on the KDDCUP99 and CICIDS2017 datasets all exceeded 99.85%, while on the UNSW-NB15 dataset surpassed 99.20%, indicating the method’s ability to accurately identify network intrusions.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

Data availability

The data can be obtained by direct messaging the author.

References

  1. Brilingaitė A et al (2022) Overcoming information-sharing challenges in cyber defence exercises. J Cybersecur 8(1):001

    Article  Google Scholar 

  2. Khraisat A, Gondal I, Vamplew P et al (2019) Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity 2:20. https://doi.org/10.1186/s42400-019-0038-7

    Article  Google Scholar 

  3. Aldweesh A, Derhab A, Emam AZ (2020) Deep learning approaches for anomaly-based intrusion detection systems: a survey, taxonomy, and open issues. Knowl Based Syst 189:105124

    Article  Google Scholar 

  4. Vinayakumar R, Soman KP, Poornachandran P (2017) Applying convolutional neural network for network intrusion detection. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE

  5. Dina AS, Manivannan D (2021) Intrusion detection based on machine learning techniques in computer networks. Internet Things 16:100462

    Article  Google Scholar 

  6. Zhou Y, Cheng G, Jiang S, Dai M (2020) Building an efficient intrusion detection system based on feature selection and ensemble classifier. Comput Netw 174:107247

    Article  Google Scholar 

  7. Mulyanto M, Faisal M, Prakosa SW, Leu JS (2021) Effectiveness of focal loss for minority classification in network intrusion detection systems. Symmetry 13(1):4

    Article  Google Scholar 

  8. Yang Z et al (2022) A systematic literature review of methods and datasets for anomaly-based network intrusion detection. Comput Secur 116:102675

    Article  Google Scholar 

  9. Pawlicki M, Choraś M, Kozik R (2020) Defending network intrusion detection systems against adversarial evasion attacks. Future Gener Comput Syst 110:148–154

    Article  Google Scholar 

  10. Dong S, Wang Abbas K (2021) A survey on deep learning and its applications. Comput Sci Rev 40:100379

    Article  MathSciNet  Google Scholar 

  11. Al-Turaiki I, Altwaijry N (2021) A convolutional neural network for improved anomaly-based network intrusion detection. Big Data 9(3):233–252

    Article  Google Scholar 

  12. Aleesa AM et al (2020) Review of intrusion detection systems based on deep learning techniques: coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions. Neural Comput Appl 32:9827–9858

    Article  Google Scholar 

  13. Otoum Y, Liu D, Nayak A (2022) DL-IDS: a deep learning–based intrusion detection framework for securing IoT. Trans Emerg Telecommun Technol 33(3):3803

    Article  Google Scholar 

  14. Li X, Chen W, Zhang Q, Wu L (2020) Building auto-encoder intrusion detection system based on random forest feature selection. Comput Secur 95:101851

    Article  Google Scholar 

  15. Wang W, Du X, Shan D, Qin R, Wang N (2022) Cloud intrusion detection method based on stacked contractive auto-encoder and support vector machine. IEEE Trans Cloud Comput 10:1634–1646. https://doi.org/10.1109/TCC.2020.300101

    Article  Google Scholar 

  16. Salih AA et al (2021) Deep learning approaches for intrusion detection. Asian J Res Comput Sci 9(4):50–64

    Article  Google Scholar 

  17. Huang X, Li Y, Ou L, Shu F, Ma W (2022) Research and implementation of industrial control network security intrusion detection classification based on deep learning. In: 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), pp 750–754. https://doi.org/10.1109/ITAIC54216.2022.9836915

  18. Alowolodu OD, Adetunmbi AO, Mebawondu JO, Mebawondu OJ (2022) A review of deep learning techniques for network intrusions detection towards efficient model developments. In: 2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON), pp 1–5. https://doi.org/10.1109/NIGERCON54645.2022.9803080

  19. Sharipuddin S, Purnama B, Kurniabudi K, Winanto E, Stiawan D, Hanapi D, Idris MYB, Budiarto R (2021) Intrusion detection with deep learning on internet of things heterogeneous network. IAES Int J Artif Intell 10:735–742. https://doi.org/10.11591/IJAI.V10.I3.PP735-742

    Article  Google Scholar 

  20. Choubey A, Krishna AVN (2021) Intrusion detection system using deep learning methodologies. J Math Comput Sci. https://doi.org/10.28919/jmcs/5921

    Article  Google Scholar 

  21. Tang Z, Hu H, Xu C (2021) A federated learning method for network intrusion detection. Concurr Comput Pract Exp. https://doi.org/10.1002/cpe.6812

    Article  Google Scholar 

  22. Kumar GSC et al (2024) Deep residual convolutional neural network: an efficient technique for intrusion detection system. Expert Syst Appl 238:121912

    Article  Google Scholar 

  23. Zhang Y et al (2023) Intrusion detection of industrial internet-of-things based on reconstructed graph neural networks. IEEE Trans Netw Sci Eng. https://doi.org/10.1109/TNSE.2022.3184975

    Article  MathSciNet  Google Scholar 

  24. Zhao R et al (2023) Semisupervised federated-learning-based intrusion detection method for internet of things. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2022.3175918

    Article  Google Scholar 

  25. Alladi T et al (2023) Ambient intelligence for securing intelligent vehicular networks: edge-enabled intrusion and anomaly detection strategies. IEEE Internet Things Mag. https://doi.org/10.1109/IOTM.001.2200197

    Article  Google Scholar 

  26. Arisdakessian S et al (2023) A survey on IoT intrusion detection: federated learning, game theory, social psychology, and explainable AI as future directions. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2022.3203249

    Article  Google Scholar 

  27. Pinto A et al (2023) Survey on intrusion detection systems based on machine learning techniques for the protection of critical infrastructure. Sensors 23(5). https://www.mdpi.com/1424-8220/23/5/2415

  28. Awajan A (2023) A novel deep learning-based intrusion detection system for IoT networks. Computers. https://doi.org/10.3390/computers12020034

    Article  Google Scholar 

  29. Wang C-H, al (Year) A novel data-driven integrated detection method for network intrusion classification based on multi-feature imbalanced data. J Intell Fuzzy Syst, 1–18. Preprint

  30. Ghosh T, Kirby M (2022) Supervised dimensionality reduction and visualization using centroid-encoder. J Mach Learn Res 23(20):1–34

    MathSciNet  Google Scholar 

  31. Irsoy O, Alpaydın E (2017) Unsupervised feature extraction with autoencoder trees. Neurocomputing 258:63–73

    Article  Google Scholar 

  32. Vincent P et al (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning—ICML ’08, pp 1096–1103. https://doi.org/10.1145/1390156.1390294

  33. Andresini G, Appice A, Malerba D (2021) Nearest cluster-based intrusion detection through convolutional neural networks. Knowl Based Syst 216:106798

    Article  Google Scholar 

  34. Porto seguro’s safe driver prediction (2021) Accessed 10 Jun 2021. https://kaggle.com/c/porto-seguro-safe-driver-prediction

  35. Yang J, Li H, Shao S, Zou F, Wu Y (2022) FS-IDS: a framework for intrusion detection based on few-shot learning

  36. Rao YN, Babu KS (2023) An imbalanced generative adversarial network-based approach for network intrusion detection in an imbalanced dataset. Sensors 23(1):550

    Article  Google Scholar 

  37. Yao W, Shi H, Zhao H (2023) Scalable anomaly-based intrusion detection for secure internet of things using generative adversarial networks in fog environment. J Netw Comput Appl 214:103622

    Article  Google Scholar 

  38. Tanimu JJ et al (2022) Network intrusion detection system using deep learning method with KDD Cup’99 dataset. In: 2022 IEEE 15th International Symposium on Embedded Multicore/Many-Core Systems-On-Chip (MCSoC). IEEE

  39. Wang Z, Liu J, Sun L (2022) EFS-DNN: an ensemble feature selection-based deep learning approach to network intrusion detection system. Secur Commun Netw 2022:2693948

    Google Scholar 

  40. Thakkar A, Lohiya R (2023) Fusion of statistical importance for feature selection in deep neural network-based intrusion detection system. Inf Fusion 90:353–363

    Article  Google Scholar 

  41. Kanna PR, Santhi P (2022) Hybrid intrusion detection using mapreduce based black widow optimized convolutional long short-term memory neural networks. Expert Syst Appl 194:116545

    Article  Google Scholar 

  42. Sharma B et al (2023) Anomaly based network intrusion detection for IoT attacks using deep learning technique. Comput Electr Eng 107:108626

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Software, Henan University, Kaifeng, 475000, Henan, China

    Hao Yang, Junyang Yu & Rui Zhai

Authors
  1. Hao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junyang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rui Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.H and Y.J.Y authored the main manuscript text, designed and analyzed the experiments, and visualized the experimental results. Z.R reviewed and revised the initial draft.

Corresponding author

Correspondence to Rui Zhai.

Ethics declarations

Competing interests

The authors declare no competing interests.

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

Yang, H., Yu, J. & Zhai, R. High-precision intrusion detection for cybersecurity communications based on multi-scale convolutional neural networks. J Supercomput 81, 277 (2025). https://doi.org/10.1007/s11227-024-06737-y

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-024-06737-y

Keywords

Search

Navigation