Classifying multiclass relationships between ASes using graph convolutional network

Songtao PENG, Xincheng SHU, Zhongyuan RUAN, Zegang HUANG, Qi XUAN

PDF(3767 KB)
PDF(3767 KB)
Front. Eng ›› 2022, Vol. 9 ›› Issue (4) : 653-667. DOI: 10.1007/s42524-022-0217-1
RESEARCH ARTICLE

Classifying multiclass relationships between ASes using graph convolutional network

Author information +
History +

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%.

Graphical abstract

Keywords

autonomous system / multiclass relationship / graph convolutional network / classification algorithm / Internet topology

Cite this article

Download citation ▾
Songtao PENG, Xincheng SHU, Zhongyuan RUAN, Zegang HUANG, Qi XUAN. Classifying multiclass relationships between ASes using graph convolutional network. Front. Eng, 2022, 9(4): 653‒667 https://doi.org/10.1007/s42524-022-0217-1

References

[1]
ApostolakiMMartiGMüllerJVanbeverL (2018). SABRE: Protecting Bitcoin against routing attacks. arXiv preprint. arXiv:1808.06254
[2]
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
CrossRef Pubmed Google scholar
[3]
BöttgerTAntichiGFernandesE LdiLallo RBruyereMUhligSCastroI (2018). The elusive Internet flattening: 10 years of IXP growth. arXiv preprint. arXiv:1810.10963
[4]
Breiman, L (2001). Random forests. Machine Learning, 45( 1): 5–32
CrossRef Google scholar
[5]
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
CrossRef Google scholar
[6]
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
CrossRef Pubmed Google scholar
[7]
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
[8]
ChenTGuestrinC (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
[9]
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
[10]
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
[11]
Cortes, C Vapnik, V (1995). Support-vector networks. Machine Learning, 20( 3): 273–297
CrossRef Google scholar
[12]
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
[13]
DiBattista GPatrignaniMPizzoniaM (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
[14]
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
CrossRef Google scholar
[15]
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
CrossRef Google scholar
[16]
Gao, L (2001). On inferring autonomous system relationships in the Internet. IEEE/ACM Transactions on Networking, 9( 6): 733–745
CrossRef Google scholar
[17]
GillPArlittMLiZMahantiA (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
[18]
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
CrossRef Google scholar
[19]
GiotsasVLuckieMHuffakerBClaffyK (2014). Inferring complex AS relationships. In: Proceedings of the Conference on Internet Measurement Conference. Vancouver, BC: Association for Computing Machinery, 23–30
[20]
GiotsasVZhouS (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
[21]
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
[22]
JinYScottCDhamdhereAGiotsasVKrishnamurthyAShenkerS (2019). Stable and practical AS relationship inference with ProbLink. In: Proceedings of the 16th USENIX Conference on Networked Systems Design and Implementation. Boston, MA: USENIX Association, 581–597
[23]
JinZShiXYangYYinXWangZWuJ (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
[24]
Karlin, J Forrest, S Rexford, J (2008). Autonomous security for autonomous systems. Computer Networks, 52( 15): 2908–2923
CrossRef Google scholar
[25]
Katz-BassettEChoffnesD RCunhaÍScottCAndersonTKrishnamurthyA (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
[26]
KeGMengQFinleyTWangTChenWMaWYeQLiuT 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
[27]
KingmaD PBaJ (2014). Adam: A method for stochastic optimization. arXiv preprint. arXiv:1412.6980
[28]
KipfT NWellingM (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint. arXiv:1609.02907
[29]
Labovitz, C Iekel-Johnson, S McPherson, D Oberheide, J Jahanian, F (2010). Internet inter-domain traffic. Computer Communication Review, 40( 4): 75–86
CrossRef Google scholar
[30]
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
[31]
Motamedi, R Rejaie, R Willinger, W (2015). A survey of techniques for Internet topology discovery. IEEE Communications Surveys and Tutorials, 17( 2): 1044–1065
CrossRef Google scholar
[32]
OrsiniCKingAGiordanoDGiotsasVDainottiA (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
[33]
PaszkeAGrossSChintalaSChananGYangEDeVitoZLinZDesmaisonAAntigaLLererA (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
[34]
Peterson, L E (2009). K-nearest neighbor. Scholarpedia, 4( 2): 1883
CrossRef Google scholar
[35]
Qiu, S Y McDaniel, P D Monrose, F (2007). Toward valley-free inter-domain routing. In: IEEE International Conference on Communications. Glasgow: IEEE, 2009–2016
[36]
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
[37]
SmithJ MSchuchardM (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
[38]
Susan VargheseJRuanL (2015). A machine learning approach to edge type prediction in Internet AS graphs. Online Paper
[39]
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
[40]
Tozal, M E (2018). Policy-preferred paths in AS-level Internet topology graphs. Theory and Applications of Graphs, 5( 1): 3
CrossRef Google scholar
[41]
van der Maaten, L Hinton, G (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9( 86): 2579–2605

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.

RIGHTS & PERMISSIONS

2022 Higher Education Press
AI Summary AI Mindmap
PDF(3767 KB)

Accesses

Citations

Detail

Sections
Recommended

/