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Classifying multiclass relationships between ASes using graph convolutional network

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Abstract

Precisely understanding the business relationships between autonomous systems (ASes) is essential for studying the Internet structure. To date, many inference algorithms, which mainly focus on peer-to-peer (P2P) and provider-to-customer (P2C) binary classification, have been proposed to classify the AS relationships and have achieved excellent results. However, business-based sibling relationships and structure-based exchange relationships have become an increasingly nonnegligible part of the Internet market in recent years. Existing algorithms are often difficult to infer due to the high similarity of these relationships to P2P or P2C relationships. In this study, we focus on multiclassification of AS relationship for the first time. We first summarize the differences between AS relationships under the structural and attribute features, and the reasons why multiclass relationships are difficult to be inferred. We then introduce new features and propose a graph convolutional network (GCN) framework, AS-GCN, to solve this multiclassification problem under complex scenes. The proposed framework considers the global network structure and local link features concurrently. Experiments on real Internet topological data validate the effectiveness of our method, that is, AS-GCN. The proposed method achieves comparable results on the binary classification task and outperforms a series of baselines on the more difficult multiclassification task, with an overall metrics above 95%.

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References

  • Apostolaki M, Marti G, Müller J, Vanbever L (2018). SABRE: Protecting Bitcoin against routing attacks. arXiv preprint. arXiv:1808.06254

  • Arber S, Hunter J J, Ross Jr J, Hongo M, Sansig G, Borg J, Perriard J C, Chien K R, Caroni P (1997). MLP-deficient mice exhibit a disruption of cardiac cytoarchitectural organization, dilated cardiomyopathy, and heart failure. Cell, 88(3): 393–403

    Article  Google Scholar 

  • Böttger T, Antichi G, Fernandes E L, di Lallo R, Bruyere M, Uhlig S, Castro I (2018). The elusive Internet flattening: 10 years of IXP growth. arXiv preprint. arXiv:1810.10963

  • Breiman L (2001). Random forests. Machine Learning, 45(1): 5–32

    Article  MATH  Google Scholar 

  • Brito S H B, Santos M A S, dos Reis Fontes R, Perez D A L, da Silva H D L, Rothenberg C R E (2016). An analysis of the largest national ecosystem of public Internet exchange points: The case of Brazil. Journal of Communication and Information Systems, 31(1): 256–271

    Article  Google Scholar 

  • Carmi S, Havlin S, Kirkpatrick S, Shavitt Y, Shir E (2007). A model of Internet topology using k-shell decomposition. Proceedings of the National Academy of Sciences of the United States of America, 104(27): 11150–11154

    Article  Google Scholar 

  • Castro I, Cardona J C, Gorinsky S, Francois P (2014). Remote peering: More peering without Internet flattening. In: Proceedings of the 10th ACM International on Conference on Emerging Networking Experiments and Technologies. Sydney: Association for Computing Machinery, 185–198

    Google Scholar 

  • Chen T, Guestrin C (2016). XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, CA: Association for Computing Machinery, 785–794

    Google Scholar 

  • Cho S, Fontugne R, Cho K, Dainotti A, Gill P (2019). BGP hijacking classification. In: Network Traffic Measurement and Analysis Conference (TMA). Paris: IEEE, 25–32

    Google Scholar 

  • Cohen A, Gilad Y, Herzberg A, Schapira M (2016). Jumpstarting BGP security with path-end validation. In: Proceedings of the ACM SIGCOMM Conference. Florianopolis: Association for Computing Machinery, 342–355

    Google Scholar 

  • Cortes C, Vapnik V (1995). Support-vector networks. Machine Learning, 20(3): 273–297

    Article  MATH  Google Scholar 

  • Dhamdhere A, Clark D D, Gamero-Garrido A, Luckie M, Mok R K P, Akiwate G, Gogia K, Bajpai V, Snoeren A C, Claffy K (2018). Inferring persistent interdomain congestion. In: Proceedings of the Conference of the ACM Special Interest Group on Data Communication. Budapest: Association for Computing Machinery, 1–15

    Google Scholar 

  • Di Battista G, Patrignani M, Pizzonia M (2003). Computing the types of the relationships between autonomous systems. In: IEEE INFOCOM. 22nd Annual Joint Conference of the IEEE Computer and Communications Societies. San Francisco, CA: IEEE, 156–165

    Google Scholar 

  • Dimitropoulos X, Krioukov D, Fomenkov M, Huffaker B, Hyun Y, Claffy K C, Riley G (2007). AS relationships: Inference and validation. Computer Communication Review, 37(1): 29–40

    Article  Google Scholar 

  • Friedman J, Hastie T, Tibshirani R (2000). Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors). Annals of Statistics, 28(2): 337–407

    Article  MathSciNet  MATH  Google Scholar 

  • Gao L (2001). On inferring autonomous system relationships in the Internet. IEEE/ACM Transactions on Networking, 9(6): 733–745

    Article  Google Scholar 

  • Gill P, Arlitt M, Li Z, Mahanti A (2008). The flattening Internet topology: Natural evolution, unsightly barnacles or contrived collapse? In: International Conference on Passive and Active Network Measurement. Cleveland, OH: Springer, 1–10

    Google Scholar 

  • Gill P, Schapira M, Goldberg S (2011). Let the market drive deployment: A strategy for transitioning to BGP security. Computer Communication Review, 41(4): 14–25

    Article  Google Scholar 

  • Giotsas V, Luckie M, Huffaker B, Claffy K (2014). Inferring complex AS relationships. In: Proceedings of the Conference on Internet Measurement Conference. Vancouver, BC: Association for Computing Machinery, 23–30

    Google Scholar 

  • Giotsas V, Zhou S (2012). Valley-free violation in Internet routing: Analysis based on BGP community data. In: IEEE International Conference on Communications (ICC). Ottawa, ON: IEEE, 1193–1197

    Google Scholar 

  • Gregori E, Improta A, Lenzini L, Rossi L, Sani L (2011). BGP and inter-AS economic relationships. In: International Conference on Research in Networking. Valencia: Springer, 54–67

    Google Scholar 

  • Jin Y, Scott C, Dhamdhere A, Giotsas V, Krishnamurthy A, Shenker S (2019). Stable and practical AS relationship inference with Prob-Link. In: Proceedings of the 16th USENIX Conference on Networked Systems Design and Implementation. Boston, MA: USENIX Association, 581–597

    Google Scholar 

  • Jin Z, Shi X, Yang Y, Yin X, Wang Z, Wu J (2020). TopoScope: Recover AS relationships from fragmentary observations. In: Proceedings of the ACM Internet Measurement Conference. New York, NY: Association for Computing Machinery, 266–280

    Google Scholar 

  • Karlin J, Forrest S, Rexford J (2008). Autonomous security for autonomous systems. Computer Networks, 52(15): 2908–2923

    Article  MATH  Google Scholar 

  • Katz-Bassett E, Choffnes D R, Cunha Í, Scott C, Anderson T, Krishnamurthy A (2011). Machiavellian routing: Improving Internet availability with BGP poisoning. In: Proceedings of the 10th ACM Workshop on Hot Topics in Networks. Cambridge, MA: Association for Computing Machinery, 1–6

    Google Scholar 

  • Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T Y (2017). LightGBM: A highly efficient gradient boosting decision tree. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, CA: Curran Associates Inc., 3149–3157

    Google Scholar 

  • Kingma D P, Ba J (2014). Adam: A method for stochastic optimization. arXiv preprint. arXiv:1412.6980

  • Kipf T N, Welling M (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint. arXiv:1609.02907

  • Labovitz C, Iekel-Johnson S, McPherson D, Oberheide J, Jahanian F (2010). Internet inter-domain traffic. Computer Communication Review, 40(4): 75–86

    Article  Google Scholar 

  • Luckie M, Huffaker B, Dhamdhere A, Giotsas V, Claffy K (2013). AS relationships, customer cones, and validation. In: Proceedings of the Conference on Internet Measurement Conference. Barcelona: Association for Computing Machinery, 243–256

    Google Scholar 

  • Motamedi R, Rejaie R, Willinger W (2015). A survey of techniques for Internet topology discovery. IEEE Communications Surveys and Tutorials, 17(2): 1044–1065

    Article  Google Scholar 

  • Orsini C, King A, Giordano D, Giotsas V, Dainotti A (2016). BGPStream: A software framework for live and historical BGP data analysis. In: Proceedings of the Internet Measurement Conference. Santa Monica, CA: Association for Computing Machinery, 429–444

    Google Scholar 

  • Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017). Automatic differentiation in PyTorch. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, CA: Curran Associates Inc., 1–4

    Google Scholar 

  • Peterson L E (2009). K-nearest neighbor. Scholarpedia, 4(2): 1883

    Article  Google Scholar 

  • Qiu S Y, McDaniel P D, Monrose F (2007). Toward valley-free interdomain routing. In: IEEE International Conference on Communications. Glasgow: IEEE, 2009–2016

    Google Scholar 

  • Shapira T, Shavitt Y (2020). Unveiling the type of relationship between autonomous systems using deep learning. In: IEEE/IFIP Network Operations and Management Symposium. Budapest: IEEE, 1–6

    Google Scholar 

  • Smith J M, Schuchard M (2018). Routing around congestion: Defeating DDoS attacks and adverse network conditions via reactive BGP routing. In: IEEE Symposium on Security and Privacy (SP). San Francisco, CA: IEEE, 599–617

    Google Scholar 

  • Susan Varghese J, Ruan L (2015). A machine learning approach to edge type prediction in Internet AS graphs. Online Paper

  • Sundaresan S, Deng X, Feng Y, Lee D, Dhamdhere A (2017). Challenges in inferring Internet congestion using throughput measurements. In: Proceedings of the Internet Measurement Conference. London: Association for Computing Machinery, 43–56

    Google Scholar 

  • Tozal M E (2018). Policy-preferred paths in AS-level Internet topology graphs. Theory and Applications of Graphs, 5(1): 3

    Article  MathSciNet  MATH  Google Scholar 

  • van der Maaten L, Hinton G (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(86): 2579–2605

    MATH  Google Scholar 

Download references

Acknowledgements

The authors would like to thank all the members of the IVSN Research Group, Zhejiang University of Technology for the valuable discussions about the ideas and technical details presented in this paper.

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Authors and Affiliations

  1. Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

    Songtao Peng, Xincheng Shu, Zhongyuan Ruan, Zegang Huang & Qi Xuan

  2. Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China

    Xincheng Shu

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  1. Songtao Peng
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  2. Xincheng Shu
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  3. Zhongyuan Ruan
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  4. Zegang Huang
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  5. Qi Xuan
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Corresponding author

Correspondence to Zhongyuan Ruan.

Additional information

This work was partially supported by the Key R&D Program of Zhejiang (Grant No. 2022C01018), the National Natural Science Foundation of China (Grant Nos. U21B2001 and 61973273), the Zhejiang Provincial Natural Science Foundation of China (Grant Nos. LY21F030017 and LR19F030001), and the Major Key Project of PCL (Grant Nos. PCL2022A03, PCL2021A02, and PCL2021A09).

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Peng, S., Shu, X., Ruan, Z. et al. Classifying multiclass relationships between ASes using graph convolutional network. Front. Eng. Manag. 9, 653–667 (2022). https://doi.org/10.1007/s42524-022-0217-1

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  • DOI: https://doi.org/10.1007/s42524-022-0217-1

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