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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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
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
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
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
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
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
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
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
Cortes C, Vapnik V (1995). Support-vector networks. Machine Learning, 20(3): 273–297
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
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
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
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
Gao L (2001). On inferring autonomous system relationships in the Internet. IEEE/ACM Transactions on Networking, 9(6): 733–745
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
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
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
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
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
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
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
Karlin J, Forrest S, Rexford J (2008). Autonomous security for autonomous systems. Computer Networks, 52(15): 2908–2923
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
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
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
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
Motamedi R, Rejaie R, Willinger W (2015). A survey of techniques for Internet topology discovery. IEEE Communications Surveys and Tutorials, 17(2): 1044–1065
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
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
Peterson L E (2009). K-nearest neighbor. Scholarpedia, 4(2): 1883
Qiu S Y, McDaniel P D, Monrose F (2007). Toward valley-free interdomain routing. In: IEEE International Conference on Communications. Glasgow: IEEE, 2009–2016
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
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
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
Tozal M E (2018). Policy-preferred paths in AS-level Internet topology graphs. Theory and Applications of Graphs, 5(1): 3
van der Maaten L, Hinton G (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(86): 2579–2605
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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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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