Adaptive multi-channel Bayesian graph attention network for IoT transaction security

Abstract

With the rapid advancement of 5G technology, the Internet of Things (IoT) has entered a new phase of applications and is rapidly becoming a significant force in promoting economic development. Due to the vast amounts of data created by numerous 5G IoT devices, the Ethereum platform has become a tool for the storage and sharing of IoT device data, thanks to its open and tamper-resistant characteristics. So, Ethereum account security is necessary for the Internet of Things to grow quickly and improve people's lives. By modeling Ethereum transaction records as a transaction network, the account types are well identified by the Ethereum account classification system established based on Graph Neural Networks (GNNs). This work first investigates the Ethereum transaction network. Surprisingly, experimental metrics reveal that the Ethereum transaction network is neither optimal nor even satisfactory in terms of accurately representing transactions per account. This flaw may significantly impede the classification capability of GNNs, which is mostly governed by their attributes. This work proposes an Adaptive Multi-channel Bayesian Graph Attention Network (AMBGAT) for Ethereum account classification to address this difficulty. AMBGAT uses attention to enhance node features, estimate graph topology that conforms to the ground truth, and efficiently extract node features pertinent to downstream tasks. An extensive experiment with actual Ethereum transaction data demonstrates that AMBGAT obtains competitive performance in the classification of Ethereum accounts while accurately estimating the graph topology.

Keywords

Internet of things / Graph representation learning / Node classification / Security mechanism

References

[1] L. Atzori, A. Iera, G. Morabito, The internet of things: a survey, Comput. Network. 54 (15) (2010) 2787-2805.

[2] K.S. Mohamed, Iot cloud computing, storage, and data analytics, in: The Era of Internet of Things, Springer, 2019, pp. 71-91.

[3] Y. Wu, Y. Lyu, Y. Shi, Cloud storage security assessment through equilibrium analysis, Tsinghua Sci. Technol. 24 (6) (2019) 738-749.

[4] M. Iansiti, K.R. Lakhani, The truth about blockchain, 01, Harv. Bus. Rev. 95 (2017) 118-127.

[5] C. Esposito, A. De Santis, G. Tortora, H. Chang, K.-K.R. Choo, Blockchain: a panacea for healthcare cloud-based data security and privacy? IEEE Cloud Computing 5 (1)(2018) 31-37.

[6] X. Liang, S. Shetty, D. Tosh, C. Kamhoua, K. Kwiat, L. Njilla, Provchain: a blockchain-based data provenance architecture in cloud environment with enhanced privacy and availability, in: 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, IEEE, 2017, pp. 468-477.

[7] H. Shafagh, L. Burkhalter, A. Hithnawi, S. Duquennoy,Towards blockchain-based auditable storage and sharing of iot data, in:Proceedings of the 2017 on Cloud Computing Security Workshop, 2017, pp. 45-50.

[8] G. Wood, et al., Ethereum: a secure decentralised generalised transaction ledger, 2014, Ethereum project yellow paper 151 (2014) 1-32.

[9] M. Yutaka, Y. Zhang, M. Sasabe, S. Kasahara, Using ethereum blockchain for distributed attribute-based access control in the internet of things, in: 2019 IEEE Global Communications Conference, IEEE, 2019, pp. 1-6.

[10] A. Raj, K. Maji, S.D. Shetty, Ethereum for internet of things security, Multimed. Tool. Appl. 80 (12) (2021) 18901-18915.

[11] S. Mehedi, A.A.M. Shamim, M.B.A. Miah, Blockchain-based security management of iot infrastructure with ethereum transactions, Iran J. Comput. Sci. 2 (3) (2019) 189-195.

[12] H. Sun, S. Hua, E. Zhou, B. Pi, J. Sun, K. Yamashita, Using ethereum blockchain in internet of things: a solution for electric vehicle battery refueling,in: International Conference on Blockchain, Springer, 2018, pp. 3-17.

[13] S. Lee, C. Yoon, H. Kang, Y. Kim, Y. Kim, D. Han, S. Son, S. Shin, Cybercriminal minds: an investigative study of cryptocurrency abuses in the dark web, in: 26TH ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM, Internet Society, 2019, pp. 1-15.

[14] L. Chen, J. Peng, Y. Liu, J. Li, F. Xie, Z. Zheng, Phishing scams detection in ethereum transaction network, ACM Trans. Internet Technol. 21 (1) (2020) 1-16.

[15] C.F. Torres, M. Steichen, et al., The art of the scam: demystifying honeypots in ethereum smart contracts,in:28th USENIX Security Symposium (USENIX Security 19), 2019, pp. 1591-1607.

[16] J. Wu, J. Liu, W. Chen, H. Huang, Z. Zheng, Y. Zhang, Detecting mixing services via mining bitcoin transaction network with hybrid motifs, IEEE Transact. Syst. Man Cybernet.: Systems 52 (4) (2022) 2237-2249c.

[17] W. Chen, Z. Zheng, J. Cui, E. Ngai, P. Zheng, Y. Zhou, Detecting ponzi schemes on ethereum: towards healthier blockchain technology,in: Proceedings of the 2018 World Wide Web Conference, 2018, pp. 1409-1418.

[18] H. Chen, M. Pendleton, L. Njilla, S. Xu, A survey on ethereum systems security: vulnerabilities, attacks, and defenses, ACM Comput. Surv. 53 (3) (2020) 1-43.

[19] S. Farrugia, J. Ellul, G. Azzopardi, Detection of illicit accounts over the ethereum blockchain, Expert Syst. Appl. 150 (2020) 113318.

[20] T. Hu, X. Liu, T. Chen, X. Zhang, X. Huang, W. Niu, J. Lu, K. Zhou, Y. Liu, Transaction-based classification and detection approach for ethereum smart contract, Inf. Process. Manag. 58 (2) (2021) 102462.

[21] W. Chen, X. Guo, Z. Chen, Z. Zheng, Y. Lu, Phishing scam detection on ethereum: towards financial security for blockchain ecosystem, in: IJCAI, 2020, pp. 4506-4512.

[22] Y. Wang, Z. Liu, J. Xu, W. Yan, Heterogeneous network representation learning approach for ethereum identity identification, IEEE Transact. Comput. Soc. Syst.(2022) 1-10, https://doi.org/10.1109/TCSS.2022.3164719.

[23] T.N. Kipf, M. Welling,Semi-supervised classification with graph convolutional networks, in:Proceedings of the International Conference on Learning Representations, 2017.

[24] P.W. Holland, K.B. Laskey, S. Leinhardt, Stochastic blockmodels: first steps, Soc. Network. 5 (2) (1983) 109-137.

[25] J. Bruna, W. Zaremba, A. Szlam, Y. LeCun,Spectral networks and deep locally connected networks on graphs, in:Proceedings of the International Conference on Learning Representations, 2014.

[26] M. Defferrard, X. Bresson, P. Vandergheynst,Convolutional neural networks on graphs with fast localized spectral filtering, Adv. Neural Inf. Process. Syst. 29 (2016) 3844-3852.

[27] L.W. Hamilton, R. Ying, J. Leskovec, Inductive representation learning on large graphs, Adv. Neural Inf. Process. Syst. 30 (2017) 1024-1034.

[28] J. Chen, T. Ma, C. Xiao, Fastgcn: fast learning with graph convolutional networks via importance sampling,in: Proceedings of the International Conference on Learning Representations, 2018.

[29] C. Wang, S. Pan, G. Long, X. Zhu, J. Jiang, Mgae: Marginalized graph autoencoder for graph clustering,in: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017, pp. 889-898.

[30] T. N. Kipf,M. Welling, Variational graph auto-encoders, arXivpreprintarXiv : 1611.07308.

[31] C. Li, M. Welling, J. Zhu, B. Zhang, Graphical generative adversarial networks, Adv. Neural Inf. Process. Syst. 31.

[32] W. Liu, P.-Y. Chen, F. Yu, T. Suzumura, G. Hu, Learning graph topological features via gan, IEEE Access 7 (2019) 21834-21843.

[33] Y. Li, R. Zemel, M. Brockschmidt, D. Tarlow,Gated graph sequence neural networks, in:Proceedings of International Conference on Learning Representations, 2016.

[34] D. Xu, W. Cheng, D. Luo, X. Liu, X. Zhang,Spatio-temporal attentive rnn for node classification in temporal attributed graphs, in:International Joint Conference on Artificial Intelligence, 2019, pp. 3947-3953.

[35] P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, Y. Bengio,Graph attention networks, in:Proceedings of the International Conference on Learning Representations, 2018.

[36] A. Salehi, H. Davulcu, Graph attention auto-encoders, in: 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence, 2020, pp. 989-996.

[37] K. Do, T. Tran, T. Nguyen, S. Venkatesh, Attentional multilabel learning over graphs: a message passing approach, Mach. Learn. 108 (10) (2019) 1757-1781.

[38] R. Wang, S. Mou, X. Wang, W. Xiao, Q. Ju, C. Shi, X. Xie, Graph structure estimation neural networks, Proceed. Web Conf. 2021 (2021) 342-353.

[39] Y. Zhang, S. Pal, M. Coates, D. Ustebay,Bayesian graph convolutional neural networks for semi-supervised classification, Proc. AAAI Conf. Artif. Intell. 33 (2019) 5829-5836.

[40] C. Zheng, B. Zong, W. Cheng, D. Song, J. Ni, W. Yu, H. Chen, W. Wang,Robust graph representation learning via neural sparsification, in:International Conference on Machine Learning, 2020, pp. 11458-11468.

[41] N. Liu, X. Wang, L. Wu, Y. Chen, X. Guo, C. Shi, Compact graph structure learning via mutual information compression, Proceed. ACM Web Conf. 2022 (2022) 1601-1610.

[42] W. Jin, Y. Ma, X. Liu, X. Tang, S. Wang, J. Tang,Graph structure learning for robust graph neural networks, in:Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2020, pp. 66-74.

[43] J. You, R. Ying, J. Leskovec,Position-aware graph neural networks, in:International Conference on Machine Learning, 2019, pp. 7134-7143.

[44] S. Zhu, J. Li, H. Peng, S. Wang, L. He,Adversarial directed graph embedding, Proc. AAAI Conf. Artif. Intell. 35 (2021) 4741-4748.

[45] R. Li, Z. Liu, Y. Ma, D. Yang, S. Sun, Internet financial fraud detection based on graph learning, IEEE Transact. Comput. Soc. Syst. (2022) 1-8, https://doi.org/ 10.1109/TCSS.2022.3189368.

[46] Z. Gu, D. Lin, J. Zheng, J. Wu, C. Hu, Deep learning-based transaction prediction in ethereum, in: International Conference on Blockchain and Trustworthy Systems, Springer, 2021, pp. 30-43.

[47] J. Liu, J. Zheng, J. Wu, Z. Zheng,Fa-gnn: Filter and Augment Graph Neural Networks for Account Classification in Ethereum, IEEE Transactions on Network Science and Engineering.

[48] Q. Yuan, B. Huang, J. Zhang, J. Wu, H. Zhang, X. Zhang, Detecting phishing scams on ethereum based on transaction records, in: 2020 IEEE International Symposium on Circuits and Systems, IEEE, 2020, pp. 1-5.

[49] J. Wu, Q. Yuan, D. yan Lin, W. You, W. Chen, C. Chen, Z. Zheng, Who are the phishers? phishing scam detection on ethereum via network embedding, IEEE Transact. Syst. Man Cybern.: Syst. 52 (2019) 1156-1166, https://doi.org/10.1109/TSMC.2020.3016821.

[50] W. Chen, X. Guo, Z. Chen, Z. Zheng, Y. Lu, Phishing scam detection on ethereum: towards financial security for blockchain ecosystem,in: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, 2021, pp. 4506-4512.

[51] H. Wen, J. Fang, J. Wu, Z. Zheng, Transaction-based hidden strategies against general phishing detection framework on ethereum, in: 2021 IEEE International Symposium on Circuits and Systems, IEEE, 2021, pp. 1-5.

[52] Z. Yuan, Q. Yuan, J. Wu, Phishing detection on ethereum via learning representation of transaction subgraphs, in: International Conference on Blockchain and Trustworthy Systems, Springer, 2020, pp. 178-191.

[53] B. Perozzi, R. Al-Rfou, S. Skiena, Deepwalk: online learning of social representations,in: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014, pp. 701-710.

[54] A. Grover, J. Leskovec, node2vec: scalable feature learning for networks,in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 855-864.

[55] Z. Meng, S. Liang, H. Bao, X. Zhang,Co-embedding attributed networks, in:Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019, pp. 393-401.

[56] T. Huang, Y. Zhang, J. Wu, J. Fang, Z. Zheng, Mg-gcn: fast and effective learning with mix-grained aggregators for training large graph convolutional networks, ArXiv abs/2011.09900.

[57] J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, D. Koutra,Beyond homophily in graph neural networks: current limitations and effective designs, Adv. Neural Inf. Process. Syst. 33 (2020) 7793-7804.

[58] M.E.J. Newman, Networks:an Introduction, Oxford University Press, 2010.

[59] B. Karrer, M.E. Newman, Stochastic blockmodels and community structure in networks, Phys. Rev. 83 (1) (2011) 016107.

[60] M.E. Newman, Network structure from rich but noisy data, Nat. Phys. 14 (6) (2018) 542-545.

[61] L. Song, A. Smola, A. Gretton, K.M. Borgwardt, J. Bedo,Supervised feature selection via dependence estimation, in:International Conference on Machine Learning, 2007, pp. 823-830.

[62] X. Wang, M. Zhu, D. Bo, P. Cui, C. Shi, J. Pei, Am-gcn: adaptive multi-channel graph convolutional networks,in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2020, pp. 1243-1253.

[63] D. Niu, J.G. Dy, M.I. Jordan,Multiple non-redundant spectral clustering views, in:International Conference on Machine Learning, 2010, pp. 831-838.

[64] M. Qu, Y. Bengio, J. Tang, Gmnn: graph markov neural networks,in:International Conference on Machine Learning, PMLR, 2019, pp. 5241-5250.

[65] R.M. Neal, G.E. Hinton, A view of the em algorithm that justifies incremental, sparse, and other variants, in: Learning in Graphical Models, Springer, 1998, pp. 355-368.

[66] A.P. Dempster, N.M. Laird, D.B. Rubin, Maximum likelihood from incomplete data via the em algorithm, J. Roy. Stat. Soc. B 39 (1) (1977) 1-22.

[67] S. Abu-El-Haija, B. Perozzi, A. Kapoor, N. Alipourfard, K. Lerman, H. Harutyunyan, G. Ver Steeg, A. Galstyan, Mixhop: higher-order graph convolutional architectures via sparsified neighborhood mixing,in: International Conference on Machine Learning, 2019, pp. 21-29.

[68] Y. Chen, L. Wu, M. Zaki,Iterative deep graph learning for graph neural networks: better and robust node embeddings, Adv. Neural Inf. Process. Syst. 33 (2020) 19314-19326.

[69] T. Wu, H. Ren, P. Li, J. Leskovec, Graph information bottleneck, Adv. Neural Inf. Process. Syst. 33 (2020) 20437-20448.