Skip to main content
SpringerLink
Log in

LDoS attack traffic detection based on feature optimization extraction and DPSA-WGAN

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Low-rate Denial of Service (LDoS) attacks cause severe destructiveness to network security. Moreover, they are more difficult to detect because they are more hidden and lack distinguishing features. Consequently, packets belonging to legitimate users can be misplaced. The performance of a transport system can be degraded by frequently sending short bursts of packets. An attack program generates its own traffic. Additionally, a diverse array of attack strategies is available. Traditional detection methods cannot effectively extract attack features. This increases the difficulty of detecting LDoS attacks in streams of fluctuating normal traffic. In this study, we establish a method for detecting LDoS attacks. It is constructed using Laplacian eigenmap (LE) and a Wasserstein generative adversarial network (WGAN) with a denoising penalty and sample-augmented discriminator (DPSA-WGAN) based on deep learning. First, an unsupervised deep learning network (LENet) is generated using LE. Moreover, data are preprocessed by dimensionality reduction to extract low-dimensional LDoS attacks features. Subsequently, we build the DPSA-WGAN on top of the WGAN to detect LDoS attacks. Moreover, a denoising penalty term and a sample-augmented discriminator are added to improve the LDoS attacks feature generation. The denoising penalty is employed to improve the performance of the generator. Moreover, the sample-augmented discriminator learns a more accurate classification-decision hyperplane. We test our method on NS2 and test-bed platforms. Using our method, dimensionality reduction and sample generation are demonstrated to be more effective than with other techniques. When background noise traffic is added, the precision rate increases. Moreover, the proposed method shows high efficiency and has an outstanding noise-reduction ability. Furthermore, it is highly robust and can detect LDoS attacks in real time.

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Agrawal N, Tapaswi S (2017) Defense schemes for variants of distributed denial-of-service (DDoS) attacks in cloud computing: a survey. Inf Sec J: A Global Perspective 26(2):61–73

    Google Scholar 

  2. Agrawal N, Tapaswi S (2019) Defense mechanisms against DDoS attacks in a cloud computing environment: state-of-the-art and research challenges. IEEE Commun Surv Tutor 21(4):1–27

    Article  Google Scholar 

  3. Matta V, Mauro MD, Longo M (2017) DDoS Attacks With Randomized Traffic Innovation: Botnet Identification Challenges and Strategies. IEEE Trans Inf Forensics Secur 12(99):1844–1859

    Article  Google Scholar 

  4. Lukaseder T, Maile L, Erb B (2018) SDN-assisted network-based mitigation of slow DDoS attacks, Secure Communication, (2) 102–121

  5. Chen H, Meng C, Fu Z (2020) Novel LDoS attack detection by spark-assisted correlation analysis approach in wireless sensor network. IET Inf Secur 6. https://doi.org/10.1049/iet-ifs.2018.5512

  6. Chen Z, Yeo CK, Lee BS (2018) Power Spectrum Entropy based Detection and Mitigation of Low-Rate DoS Attacks. Comput Netw 136(8):80–94

    Article  Google Scholar 

  7. Tang D, Zhang S, Chen J (2021) The detection of low-rate DoS attacks using the SADBSCAN algorithm. Inf Sci 565:229–247. https://doi.org/10.1016/j.ins.2021.02.038

    Article  MathSciNet  Google Scholar 

  8. Agrawal N, Tapaswi S (2018) Low Rate Cloud DDoS Attack Defense Method Based on Power Spectral Density Analysis. Inf Process Lett 138:44–50

    Article  MathSciNet  MATH  Google Scholar 

  9. Yue M, Liu L, Wu Z (2018) Identifying LDoS attack traffic based on wavelet energy spectrum and combined neural network. Int J Commun Syst 31(2):1–16

    Article  Google Scholar 

  10. Sahoo KS, Puthal D, Tiwary M (2018) An early detection of low rate DDoS attack to SDN based data center networks using information distance metrics. Futur Gener Comput Syst 89:685–697

    Article  Google Scholar 

  11. Liu X, Ren J, He H (2021) Low-rate DDoS attacks detection method using data compression and behavior divergence measurement. Comput Secur 100:102107

    Article  Google Scholar 

  12. Agrawal N, Tapaswi S (2020) Detection of low-rate cloud DDoS attacks in frequency domain using fast Hartley transform. Wirel Pers Commun 112(2):1735–1762

    Article  Google Scholar 

  13. Wang W, Ke X, Wang L (2018) A HMM-R approach to detect L-DDoS attack adaptively on SDN controller. Future Internet 10(9):83

    Article  Google Scholar 

  14. Lin SW, Ying KC, Lee CY (2012) An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection. Appl Soft Comput 12(10):3285–3290

    Article  Google Scholar 

  15. Ratti R, Singh SR, Nandi S (2020) Towards implementing fast and scalable Network Intrusion Detection System using Entropy based Discretization Technique. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

  16. Jiang F, Sui Y, Zhou L (2015) A relative decision entropy-based feature selection approach. Pattern Recogn 48(7):2151–2163

    Article  MATH  Google Scholar 

  17. Feng J, Sui Y (2015) A novel approach for discretization of continuous attributes in rough set theory. Knowl-Based Syst 73(6):324–334

    Google Scholar 

  18. Prabakeran S, Sethukarasi T (2020) Optimal solution for malicious node detection and prevention using hybrid chaotic particle dragonfly swarm algorithm in VANETs. Wirel Netw 26(3):5897–5917

    Article  Google Scholar 

  19. a.Baskar M, Ramkumar J, Karthikeyan C, Anbarasu V, Balaji A, Arulananth T (2021) Low rate DDoS mitigation using real-time multi threshold traffic monitoring system. J Ambient Intell Humaniz Comput:1–9

  20. Kushwah GS, Ranga V (2021) Optimized extreme learning machine for detecting DDoS attacks in cloud computing. Comput Secur 105:102260

    Article  Google Scholar 

  21. Tang D, Tang L, Shi W, Zhan S, Yang Q (2020) Mf-cnn: a new approach for ldos attack detection based on multi-feature fusion and cnn. Mob Netw Appl 7:1–18

    Google Scholar 

  22. Gopi R, Sathiyamoorthi V, Selvakumar S (2021) Enhanced method of ANN based model for detection of DDoS attacks on multimedia internet of things. Multimed Tools Appl 81:1–19

    Google Scholar 

  23. Belkin M, Niyogi P (2014) Laplacian eigenmaps for dimensionality reduction and datarepresentation. Neural Comput 15(6):1373–1396

    Article  MATH  Google Scholar 

  24. Wang T, Li W, Rong H (2022) Abnormal traffic detection-based on memory augmented generative adversarial IIoT-assisted network. Wirel Netw 28:2579–2595

    Article  Google Scholar 

  25. Kim JY, Bu SJ, Cho SB (2018) Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders[J]. Inf Sci 460-461:83–102

    Article  Google Scholar 

  26. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein gan, ar Xiv preprint ar Xiv:1701. 07875, 1–32

  27. Zhang X, Wu Z, Chen J, Yue M (2017) An adaptive kpca approach for detecting ldos attack. Int J Commun Syst 30(4):e2993

    Article  Google Scholar 

  28. Wu Z, Pan Q, Yue M, Liu L (2019) Sequence alignment detection of tcp-targeted synchronous low-rate dos attacks. Comput Netw 152:64–77

    Article  Google Scholar 

  29. Tang D, Tang L, Dai R, Chen J, Li X, Rodrigues JJ (2020) Mf-adaboost: Ldos attack detection based on multi-features and improved adaboost. Futur Gener Comput Syst 106:347–359

    Article  Google Scholar 

  30. Tang D, Man J, Tang L, Feng Y, Yang Q (2020) Wedms: an advanced mean shift clustering algorithm for ldos attacks detection. Ad Hoc Netw 102:102145

    Article  Google Scholar 

  31. Liu L, Wang H, Wu Z (2020) The detection method of low-rate DoS attack based on multi-feature fusion. Digital Commun Netw 6:504–513

    Article  Google Scholar 

  32. Liu L, Wang H, Wu Z, Yue M (2020) The detection method of low-rate dos attack based on multi-feature fusion. Digital Commun Netw 6:504–513

    Article  Google Scholar 

  33. Duy PT, Le KT, Khoa NH (2021) DIGFuPAS: Deceive IDS with GAN and Function-Preserving on Adversarial Samples in SDN-enabled networks. Computers & Security 109(1):102367

    Article  Google Scholar 

  34. Cheng J, Luo Y, Tang X, Ou M (Apr. 2019) DoS attack detection method based on LSTM traffic prediction. J Huazhong Univ Sci Technol 47(4):32–36

    Google Scholar 

  35. Tran D, Mac H, Tong V (2017) A LSTM based Framework for Handling Multiclass Imbalance in DGA Botnet Detection. Neurocomputing 275:2401–2413

    Article  Google Scholar 

  36. Zhong Y, Chen W, Wang Z (2019) HELAD: a novel network anomaly detection model based on heterogeneous ensemble learning. Comput Netw 169:107049

    Article  Google Scholar 

  37. Li Q, Wang F, Wang J (2019) LSTM-based SQL injection detection method for intelligent transportation system. IEEE Trans Veh Technol 68(5):4182–4191

    Google Scholar 

  38. Huang W, Peng X, Shi Z, Ma Y (Nov. 2020) Dversarial attack against LSTM-based DDoS intrusion detection system. In: Proc. IEEE 32nd Int.Conf. Tools with Artif. Intell. (ICTAI), Baltimore, MD, USA, pp: 686–693

  39. Binbusayyis A, Vaiyapuri T (2021) Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM. Appl Intell 8:7094–7108

    Article  Google Scholar 

  40. Eynard D, Kovnatsky A, Bronstein MM (2015) Multimodal manifold analysis by simultaneous diagonalization of laplacians. IEEE Trans Pattern Anal Mach Intell 37(12):2505–2517

    Article  Google Scholar 

  41. Odena A, Buckman J, Olsson C (2018) Is generator conditioning causally related to Gan performance. International conference on machine learning (ICML), Stockholm, Sweden, pp: 3846–3855

  42. Chen T, Lucic M, Houlsby N (2018) On self modulation for generative adversarial networks, ar Xiv preprint ar Xiv:1810.01365 1–18

  43. Hou M, Chaib-Draa B, Li C (2018) Generative adversarial positive-unlabeled learning. Proceedings of the 27th international joint conference on artificial intelligence (IJCAI), Stockholm, Sweden, 2255–2261

  44. Li H, Zhu J, Wang Q, Zhou T, Qiu H, Li H (2016) LAAEM: a method to enhance LDoS attack. IEEE Commun Lett 20(4):708–711

    Article  Google Scholar 

  45. Kuzmanovic A, Knightly EW (2003) Low-rate TCP-targeted denial of service attacks. Proc ACM SIGCOMM 14(4):75–86

    Article  Google Scholar 

  46. Mao X, Li Q, Xie H (2017) Least squares generative adversarial networks. Proceedings of the IEEE international conference on computer vision (CVPR), Hawaii, America, pp: 2794–2802

  47. Xu Z, Fang X, Yang G (2021) Malbert: a novel pre-training method for malware detection. Comput Secur 111:102458. https://doi.org/10.1016/j.cose.2021.102458

    Article  Google Scholar 

Download references

Acknowledgments

This research is funded by the National Natural Science Foundation of China, grant number 61703349. Moreover, it is funded by the Science and Technology Research and Development Project, grant number N2018G062, K2018G011. In the end, it is funded by the Science and technology research and development plan of China National Railway Group, grant number L2021X001.

Declaration

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China

    Wengang Ma, Ruiqi Liu & Jin Guo

  2. School of Electrical Electronic & Computer Science, Guangxi University of Science and Technology, Liuzhou, 545006, China

    Ruiqi Liu

Authors
  1. Wengang Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruiqi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jin Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruiqi 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 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

Ma, W., Liu, R. & Guo, J. LDoS attack traffic detection based on feature optimization extraction and DPSA-WGAN. Appl Intell 53, 13924–13955 (2023). https://doi.org/10.1007/s10489-022-04171-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-04171-2

Keywords

Search

Navigation