Computer Engineering, Volume. 51, Issue 8, 215(2025)

HGNM: Long-Short Term Flow Graph and Hybrid Graph Neural Network-based Saturation Attack Detection Method

LI Jiasong1,2,3, CUI Yunhe1,2,3、*, SHEN Guowei1,2,3, GUO Chun1,2,3, CHEN Yi1,2,3, and JIANG Chaohui1,2,3
Author Affiliations
  • 1Engineering Research Center of Ministry of Education for Text Computing and Cognitive Intelligence, School of Computer Science and Technology, Guizhou University, Guiyang 550025, Guizhou, China
  • 2State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, Guizhou, China
  • 3Provincial Key Laboratory of Software Engineering and Information Security, Guizhou University, Guiyang 550025, Guizhou, China
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    References(26)

    [1] [1] MALEH Y, QASMAOUI Y, EL GHOLAMI K, et al. A comprehensive survey on SDN security: threats, mitigations, and future directions[J]. Journal of Reliable Intelligent Environments, 2023, 9(2): 201-239.

    [3] [3] CONTI M, GANGWAL A, GAUR M S. A comprehensive and effective mechanism for DDoS detection in SDN[C]//Proceedings of the 13th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). Washington D.C., USA: IEEE Press, 2017: 1-8.

    [5] [5] KANNAN K, BANERJEE S. Compact TCAM: flow entry compaction in TCAM for power aware SDN[EB/OL]. [2024-02-05]. https://link.springer.com/chapter/10.1007/978-3-642-35668-1_32.

    [6] [6] VISHNOI A, PODDAR R, MANN V, et al. Effective switch memory management in OpenFlow networks[C]//Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems. New York, USA: ACM Press, 2014: 177-188.

    [7] [7] KANDOI R, ANTIKAINEN M. Denial-of-service attacks in OpenFlow SDN networks[C]//Proceedings of the IFIP/IEEE International Symposium on Integrated Network Management (IM). Washington D.C., USA: IEEE Press, 2015: 1322-1326.

    [10] [10] DENG A L, HOOI B. Graph neural network-based anomaly detection in multivariate time series[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(5): 4027-4035.

    [11] [11] ZHENG J W, LI D G. GCN-TC: combining trace graph with statistical features for network traffic classification[C]//Proceedings of the 2019 IEEE International Conference on Communications (ICC). Washington D.C., USA: IEEE Press, 2019: 1-6.

    [12] [12] NAGARAJ K, STARKE A, MCNAIR J. GLASS: a graph learning approach for software defined network based smart grid DDoS security[C]//Proceedings of the IEEE International Conference on Communications. Washington D.C., USA: IEEE Press, 2021: 1-6.

    [13] [13] CAO Y Y, JIANG H, DENG Y C, et al. Detecting and mitigating DDoS attacks in SDN using spatial-temporal graph convolutional network[J]. IEEE Transactions on Dependable and Secure Computing, 2021, 19(6): 3855-3872.

    [14] [14] WANG K X, CUI Y H, QIAN Q, et al. USAGE: uncertain flow graph and spatio-temporal graph convolutional network-based saturation attack detection method[J]. Journal of Network and Computer Applications, 2023, 219: 103722.

    [15] [15] DU R Y, HUANG M H, LIU F L. Multi-classification algorithm based on graph convolutional neural network for intrusion detection[J]. Signal, Image and Video Processing, 2025, 19(6): 493.

    [16] [16] SUN B Y, YANG W Y, YAN M Q, et al. An encrypted traffic classification method combining graph convolutional network and autoencoder[C]//Proceedings of the 39th IEEE International Performance Computing and Communications Conference (IPCCC). Washington D.C., USA: IEEE Press, 2020: 1-8.

    [17] [17] RAN L Y, CUI Y H, GUO C, et al. Defending saturation attacks on SDN controller: a confusable instance analysis-based algorithm[J]. Computer Networks, 2022, 213: 109098.

    [18] [18] XIAO M, CUI Y H, QIAN Q, et al. KIND: a novel image-mutual-information-based decision fusion method for saturation attack detection in SD-IoT[J]. IEEE Internet of Things Journal, 2022, 9(23): 23750-23771.

    [19] [19] AHALAWAT A, BABU K S, TURUK A K, et al. A low-rate DDoS detection and mitigation for SDN using Renyi entropy with packet drop[J]. Journal of Information Security and Applications, 2022, 68: 103212.

    [20] [20] ASSIS M V O, CARVALHO L F, LLORET J, et al. A GRU deep learning system against attacks in software defined networks[J]. Journal of Network and Computer Applications, 2021, 177: 102942.

    [21] [21] SAID ELSAYED M, LE-KHAC N A, DEV S, et al. Network anomaly detection using LSTM based autoencoder[C]//Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks. New York, USA: ACM Press, 2020: 37-45.

    [22] [22] KALKAN K, ALTAY L, GUR G, et al. JESS: joint entropy-based DDoS defense scheme in SDN[J]. IEEE Journal on Selected Areas in Communications, 2018, 36(10): 2358-2372.

    [23] [23] LI Z Y, XING W J, KHAMAISEH S, et al. Detecting saturation attacks based on self-similarity of OpenFlow traffic[J]. IEEE Transactions on Network and Service Management, 2019, 17(1): 607-621.

    [24] [24] Open networking foundation: OpenFlow switch specification 1.3.0[EB/OL]. [2024-02-05]. http://www.cs.yale.edu/homes/yuminlan/teach/csci599-fall12/papers/openflow-spec-v1.3.0.pdf.

    [25] [25] ZHOU Y D, LI H, CHEN K Y, et al. Raze policy conflicts in SDN[J]. Journal of Network and Computer Applications, 2022, 199: 103307.

    [26] [26] LEI K, LIN G J, ZHANG M M, et al. Measuring the consistency between data and control plane in SDN[J]. ACM Transactions on Networking, 2022, 31(2): 511-525.

    [28] [28] CUI Y H, YAN L S, LI S F, et al. SD-anti-DDoS: fast and efficient DDoS defense in software-defined networks[J]. Journal of Network and Computer Applications, 2016, 68: 65-79.

    [29] [29] PENG J C, CUI Y H, QIAN Q, et al. ADVICE: towards adaptive scheduling for data collection and DDoS detection in SDN[J]. Journal of Information Security and Applications, 2021, 63: 103017.

    [30] [30] Nmap: the network mapper-free security scanner[EB/OL]. [2024-02-05]. https://nmap.org/nping/.

    [31] [31] ZHAO L, SONG Y J, ZHANG C, et al. T-GCN: a temporal graph convolutional network for traffic prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(9): 3848-3858.

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    LI Jiasong, CUI Yunhe, SHEN Guowei, GUO Chun, CHEN Yi, JIANG Chaohui. HGNM: Long-Short Term Flow Graph and Hybrid Graph Neural Network-based Saturation Attack Detection Method[J]. Computer Engineering, 2025, 51(8): 215

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    Paper Information

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    Received: Mar. 6, 2024

    Accepted: Aug. 26, 2025

    Published Online: Aug. 26, 2025

    The Author Email: CUI Yunhe (yhcui@gzu.edu.cn)

    DOI:10.19678/j.issn.1000-3428.0069494

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