Abstract
In recent years, the increasing reliance on Internet services has made the Internet an integral part of our daily life. The COVID-19 pandemic has further accelerated this trend by driving the demand for online services such as remote work, virtual meetings, and online events. However, this increasing dependence on the Internet has also made us vulnerable to various cyber threats, particularly DDoS attacks, which have become a serious issue. For this reason, researchers have proposed numerous defense mechanisms to mitigate the risks associated with DDoS attacks, among which Machine Learning (ML) based Intrusion Detection Systems (IDS) have shown promising results. Nevertheless, most existing ML-based IDSs focus on known attack features, leaving them vulnerable to attacks that utilize unknown features. To overcome this limitation, researchers propose a new concept Open-Set Recognition (OSR), which explores new approaches that modify the Deep Learning method to identify unknown patterns. Therefore, we propose a novel IDS model based on OSR to detect Unknown DDoS attacks. The model detects unknown DDoS attacks with the U-Net + Reciprocal Points Learning (RPL). With a detection rate of approximately 99%, our model can successfully identify known and unknown DDoS attacks while maintaining an ability to manage imbalanced situations.
This work was supported by National Science and Technology Council, Taiwan, grant No. MOST 111-2221-E-992-066- and MOST 109-2221-E-992-073-MY3.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Al-Jawfi, R.: Handwriting Arabic character recognition LeNet using neural network. Int. Arab J. Inf. Technol. 6(3), 304–309 (2009)
Bendale, A., Boult, T.E.: Towards open set deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1563–1572 (2016)
Chen, G., Peng, P., Wang, X., Tian, Y.: Adversarial reciprocal points learning for open set recognition. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 8065–8081 (2021)
Chen, G., et al.: Learning open set network with discriminative reciprocal points. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 507–522. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58580-8_30
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35(1), 53–65 (2018)
Douligeris, C., Mitrokotsa, A.: DDoS attacks and defense mechanisms: classification and state-of-the-art. Comput. Netw. 44(5), 643–666 (2004)
Elkan, C.: Results of the KDD’99 classifier learning. ACM SIGKDD Explor. Newsl. 1(2), 63–64 (2000)
Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin Softmax loss for convolutional neural networks. In: Proceedings of The 33rd International Conference on Machine Learning, pp. 507–516 (2016)
Maseer, Z.K., Yusof, R., Bahaman, N., Mostafa, S.A., Foozy, C.F.M.: Benchmarking of machine learning for anomaly based intrusion detection systems in the cicids2017 dataset. IEEE Access 9, 22351–22370 (2021)
Shieh, C.S., Lin, W.W., Nguyen, T.T., Chen, C.H., Horng, M.F., Miu, D.: Detection of unknown DDoS attacks with deep learning and gaussian mixture model. Appl. Sci. 11(11), 5213 (2021)
Shieh, C.S., Nguyen, T.T., Chen, C.Y., Horng, M.F.: Detection of unknown DDoS attack using reconstruct error and one-class SVM featuring stochastic gradient descent. Mathematics 11(1), 108 (2022)
Shieh, C.S., et al.: Detection of adversarial DDoS attacks using generative adversarial networks with dual discriminators. Symmetry 14(1), 66 (2022)
Siddique, N., Paheding, S., Elkin, C.P., Devabhaktuni, V.: U-net and its variants for medical image segmentation: a review of theory and applications. IEEE Access 9, 82031–82057 (2021)
Xia, Z., Wang, P., Dong, G., Liu, H.: Spatial location constraint prototype loss for open set recognition. Comput. Vis. Image Underst. 229, 103651 (2023)
Yang, H.M., Zhang, X.Y., Yin, F., Liu, C.L.: Robust classification with convolutional prototype learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3474–3482 (2018)
Yang, J., Zhou, K., Li, Y., Liu, Z.: Generalized out-of-distribution detection: a survey. arXiv preprint arXiv:2110.11334 (2021)
Yoshihashi, R., Shao, W., Kawakami, R., You, S., Iida, M., Naemura, T.: Classification-reconstruction learning for open-set recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4016–4025 (2019)
Zhou, Q., Pezaros, D.: Evaluation of machine learning classifiers for zero-day intrusion detection-an analysis on CIC-AWS-2018 dataset. arXiv preprint arXiv:1905.03685 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ho, FA., Shieh, CS., Horng, MF., Nguyen, TT., Chao, YC. (2023). Reciprocal Points Learning Based Unknown DDoS Attacks Detection. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-42430-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42429-8
Online ISBN: 978-3-031-42430-4
eBook Packages: Computer ScienceComputer Science (R0)