Abstract
The existing DDOS detection methods have the problems of single acquisition node and low detection rate. A multi-source DDOS information fusion model (HRM) based on hierarchical representation learning network and a FlowMerge algorithm based on three network flow merging modes are proposed. Firstly, the network traffic is transformed into triples, and the dimensionality reduction of Tsne algorithm is used to transform it into network IP topology structure graph. Then, the network flow is merged by FlowMerge algorithm, which is decomposed into a series of smaller and approximate coarse-grained topology structure graphs. Then, the features are embedded into more fine-grained graphs iteratively, and the HRM model is established. The experimental results show that the model can better reflect the temporal and spatial characteristics of network traffic, improve the detection accuracy, and have better robustness.
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
References
Doshi, R., Apthorpe, N., Feamster, N.: Machine Learning DDoS Detection for Consumer Internet of Things Devices, pp. 29–35 (2018)
Hodo, E., Bellekens, X., Hamilton, A., et al.: Threat analysis of IoT networks using artificial neural network intrusion detection system. In: 2016 International Symposium on Networks, Computers and Communications (ISNCC), Yasmine Hammamet, pp. 1–6 (2016)
Cheng, J., Zhou, J., Liu, Q., Tang, X., Guo, Y.: A DDoS detection method for socially aware networking based on forecasting fusion feature sequence. Comput. J. 61(7), 959–970 (2018)
Cheng, J., Xu, R., Tang, X., Sheng, V.S., Cai, C.: An abnormal network flow feature sequence prediction approach for DDoS attacks detection in big data environment. Comput. Mater. Continua 55(1), 95–119 (2018)
Cheng, J., Tang, X., Yin, J.: A change-point DDoS attack detection method based on half interaction anomaly degree. Int. J. Auton. Adapt. Commun. Syst. 10(1), 38–54 (2017)
Spaulding, J., Mohaisen, A.: Defending internet of things against malicious domain names using D-FENS. In: 2018 IEEE/ACM Symposium on Edge Computing (SEC). ACM (2018)
Nam, T.M., Phong, P.H., Khoa, T.D., et al.: [IEEE 2018 International Conference on Information Networking (ICOIN) - Chiang Mai, Thailand (2018.1.10–2018.1.12)] 2018 International Conference on Information Networking (ICOIN) - Self-organizing map-based approaches in DDoS flooding detection using SDN. In: International Conference on Information Networking, pp. 249–254. IEEE Computer Society (2018)
Li, P., Li, J., Huang, Z., et al.: Privacy-preserving outsourced classification in cloud computing. Cluster Comput. 21(1), 277–286 (2018)
Li, J., Chen, X., Huang, Q., et al.: Digital provenance: enabling secure data forensics in cloud computing. Future Gener. Comput. Syst. 37, 259–266 (2014)
Li, J., Chen, X., Chow, S.S.M., Huang, Q., Wong, D.S., Liu, Z.: Multi-authority fine-grained access control with accountability and its application in cloud. J. Netw. Comput. Appl. https://doi.org/10.1016/j.jnca.2018.03.006
Cheng, J., Yin, J., Liu, Y., Cai, Z., Li, M.: Detecting distributed denial of service attack based on address correlation value. J. Comput. Res. Dev. 46(8), 1334–1340 (2009)
Pezzotti, N., Lelieveldt, B.P.F., Maaten, L.V.D., et al.: Approximated and user steerable tSNE for progressive visual analytics. IEEE Trans. Vis. Comput. Graph 23(7), 1739–1752 (2017)
Chen, H., Perozzi, B., Hu, Y., et al.: HARP: Hierarchical Representation Learning for Networks (2017)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)
Cao, Y., Zhou, Z., Sun, X., Gao, C.: Coverless information hiding based on the molecular structure images of material. Comput. Mater. Continua 54(2), 197–207 (2018)
Agarwal, A., Dawson, S., Mckee, D., et al.: Detecting abnormalities in IoT program executions through control-flow-based features: poster abstract. In: International Conference on Internet-of-Things Design and Implementation (2017)
Doshi, R., Apthorpe, N., Feamster, N.: [IEEE 2018 IEEE Security and Privacy Workshops (SPW) - San Francisco, CA, USA (2018.5.24–2018.5.24)] 2018 IEEE Security and Privacy Workshops (SPW) - Machine Learning DDoS Detection for Consumer Internet of Things Devices, pp. 29–35 (2018)
Srihari, V., Anitha, R.: DDoS detection system using wavelet features and semi-supervised learning. In: Mauri, J.L., Thampi, S.M., Rawat, D.B., Jin, D. (eds.) SSCC 2014. CCIS, vol. 467, pp. 291–303. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44966-0_28
Acknowledgements
This work was supported by the Hainan Provincial Natural Science Foundation of China [2018CXTD333, 617048]; National Natural Science Foundation of China [61762033, 61702539]; Hainan University Doctor Start Fund Project [kyqd1328]; Hainan University Youth Fund Project [qnjj1444]; Social Development Project of Public Welfare Technology Application of Zhejiang Province [LGF18F020019]; Ministry of Education Humanities and Social Sciences Research Planning Fund Project (19YJA710010).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Tang, X., Zhang, Y., Cheng, J., Xu, J., Li, H. (2019). DDOS Multivariate Information Fusion Model Based on Hierarchical Representation Learning. In: Vaidya, J., Zhang, X., Li, J. (eds) Cyberspace Safety and Security. CSS 2019. Lecture Notes in Computer Science(), vol 11983. Springer, Cham. https://doi.org/10.1007/978-3-030-37352-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-37352-8_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37351-1
Online ISBN: 978-3-030-37352-8
eBook Packages: Computer ScienceComputer Science (R0)