Phishing Fraud Identity Inference Based on Graph Gated Recurrent Neural Network

Zhaohuang Chen; Zhongqi Fu; Tao Liang; Haidong Ma; Yanfeng Sun; Xiaohu Shi

doi:10.2478/jaiscr-2026-0013

Abstract

Since the proposal of the blockchain, its application scenarios have been continuously expanded. However, the anonymity feature of the blockchain has hindered market regulation, leading to numerous illegal activities such as phishing fraud, which has now become a serious type of crime. Currently, most phishing fraud detection technologies on blockchain platforms use transaction data to construct basic raw transaction graphs and then use neural network methods to mine key information. This study proposes a graph gated recurrent neural network (GGRNN) model that fully integrates temporal and spatial information, effectively utilizing time-related information in the transaction graph. It first takes an account as the center node to obtain its second-order transaction data and then constructs a dynamic transaction graph (DTG). Subsequently, the DTG is fed to the GGRNN to process the temporal features in a gated recurrent unit (GRU) framework and introduce graph convolutional network (GCN) operations to fully use the node neigh-bourhood topology features, obtain the embedded representation of the graph, and then perform graph classification for phishing node detection. To verify the effectiveness of the proposed model, it was applied to real-world Ethereum transaction datasets. Numerical results show that the proposed GGRNN model significantly outperforms state-of-the-art methods.

References

S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” Decentralized Business Review, 2008.
Search in Google Scholar Back to article
V. Buterin, “Ethereum: A next-generation cryptocurrency and decentralized application platform,” Bitcoin Magazine, 2014.
Search in Google Scholar Back to article
Y. Liu, Z. Ai, S. Sun, S. Zhang, Z. Liu, and H. Yu, FedCoin: A Peer-to-Peer Payment System for Federated Learning, in Federated Learning - Privacy and Incentive (Lecture Notes in Computer Science, Vol. 12500), Springer, pp. 125–138, 2020. DOI: https://doi.org/10.1007/978-3-030-63076-89.
Search in Google Scholar Back to article
H. Sami Ullah and S. Aslam, Blockchain in Healthcare and Medicine: A Contemporary Research of Applications, Challenges, and Future Perspectives, in CoRR, Vol. abs/2004.06795, 2020. arXiv: https://arxiv.org/abs/2004.06795, eprint: 2004.06795.
Search in Google Scholar Back to article
T. Alam and M. Benaida, Blockchain, Fog and IoT Integrated Framework: Review, Architecture and Evaluation, in CoRR, Vol. abs/2006.03596, 2020. arXiv: https://arxiv.org/abs/2006.03596, eprint: 2006.03596.
Search in Google Scholar Back to article
D. Perard, L. Gicquel, and J. Lacan, Block-House: Blockchain-Based Distributed Storehouse System, in 9th Latin-American Symposium on Dependable Computing (LADC 2019), Natal, Brazil, November 19-21, 2019, IEEE, pp. 1–4, 2019. DOI: https://doi.org/10.1109/LADC48089.2019.8995675.
Search in Google Scholar Back to article
D. Zhang, J. Chen, and X. Lu, Blockchain Phishing Scam Detection via Multi-channel Graph Classification, in Blockchain and Trustworthy Systems - Third International Conference, BlockSys 2021 (Communications in Computer and Information Science, Vol. 1490), edited by Hong-Ning Dai, Xuanzhe Liu, Daniel Xiapu Luo, Jiang Xiao, and Xiangping Chen, Guangzhou, China, August 5-6, 2021, Revised Selected Papers, Springer, pp. 241–256, 2021. DOI: https://doi.org/10.1007/978-981-16-7993-3_19.
Search in Google Scholar Back to article
J. Zhou, C. Hu, J. Chi, J. Wu, M. Shen, and Q. Xuan, Behavior-Aware Account De-Anonymization on Ethereum Interaction Graph, in IEEE Trans. Inf. Forensics Secur., Vol. 17, pp. 3433–3448, 2022. DOI: https://doi.org/10.1109/TIFS.2022.3208471.
Search in Google Scholar Back to article
Y. Liu, C. Wang, and Y. Ma, DL4SC: a novel deep learning-based vulnerability detection framework for smart contracts, Automated Software Engineering, Vol. 31, p. 24, 2024. DOI: https://doi.org/10.1007/s10515-024-00418-z.
Search in Google Scholar Back to article
H. R. Ranganatha and A. Syed Mustafa, Enhancing fraud detection efficiency in mobile transactions through the integration of bidirectional 3d Quasi-Recurrent Neural network and blockchain technologies, Expert Systems with Applications, Vol. 260, p. 125179, 2025. DOI: https://doi.org/10.1016/j.eswa.2024.125179.
Search in Google Scholar Back to article
P. T. Duy, N. H. Khoa, N. H. Quyen, L. C. Trinh, V. T. Kien, T. M. Hoang, and V.-H. Pham, Vulnsense: efficient vulnerability detection in ethereum smart contracts by multimodal learning with graph neural network and language model, International Journal of Information Security, Vol. 24, No. 1, p. 48, 2025. DOI: https://doi.org/10.1007/s10207-024-00965-2.
Search in Google Scholar Back to article
A. Muniswamy and R. Rathi, Trust-Based Consensus and ABAC for Blockchain Using Deep Learning to Secure Internet of Things, Applied Artificial Intelligence, Vol. 39, No. 1, 2025. DOI: https://doi.org/10.1080/08839514.2025.2459461.
Search in Google Scholar Back to article
X. Wang, S. Tian, and W. Cui, ContractCheck: Checking Ethereum Smart Contracts in Fine-Grained Level, IEEE Transactions on Software Engineering, Vol. 50, No. 7, pp. 1789–1806, 2024. DOI: https://doi.org/10.1109/TSE.2024.3400294.
Search in Google Scholar Back to article
X. Liu, Z. Tang, P. Li, S. Guo, X. Fan, and J. Zhang, A Graph Learning Based Approach for Identity Inference in DApp Platform Blockchain, in IEEE Trans. Emerg. Top. Comput., Vol. 10, No. 1, pp. 438–449, 2022. DOI: https://doi.org/10.1109/TETC.2020.3027309.
Search in Google Scholar Back to article
H. Sui, J. Zhang, B. Chen, D. Wu, X. Sun, and S. Palaiahnakote, EPAD: Ethereum phishing scam detection via graph contrastive learning, in Expert Systems with Applications, Vol. 288, Art. No. 128227, 2025. DOI: https://doi.org/10.1016/j.eswa.2025.128227.
Search in Google Scholar Back to article
L. Wang, M. Xu, and H. Cheng, Phishing scams detection via temporal graph attention network in Ethereum, in Information Processing & Management, Vol. 60, No. 4, Art. No. 103412, 2023. DOI: https://doi.org/10.1016/j.ipm.2023.103412.
Search in Google Scholar Back to article
Z. Chen, S.-Z. Liu, J. Huang, Y.-H. Xiu, H. Zhang, and H.-X. Long, Ethereum Phishing Scam Detection Based on Data Augmentation Method and Hybrid Graph Neural Network Model, in Sensors, Vol. 24, No. 12, Art. No. 4022, 2024. DOI: https://doi.org/10.3390/s24124022
Search in Google Scholar Back to article
J. Wang, P. Chen, S. Yu, and Q. Xuan, “TSGN: Transaction subgraph networks for identifying Ethereum phishing accounts,” in BlockSys 2021, Springer, Singapore, pp. 187–200, 2021. DOI: https://doi.org/10.1007/978-981-16-7993-3_15.
Search in Google Scholar Back to article
S. Li, G. Gou, C. Liu, C. Hou, Z. Li, and G. Xiong, TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection, in Proceedings of the ACM Web Conference 2022 (WWW’22), ACM, pp. 661–669, 2022. DOI: https://doi.org/10.1145/3485447.3512226.
Search in Google Scholar Back to article
T. N. Kipf and M. Welling, Semi-Supervised Classification with Graph Convolutional Networks, in 5th International Conference on Learning Representations (ICLR 2017), Toulon, France, April 24-26, 2017, OpenReview.net, 2017. URL: https://openreview.net/forum?id=SJU4ayYgl.
Search in Google Scholar Back to article
K. Cho, B. van Merrienboer, Ç. Gülçehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation, in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), edited by A. Moschitti, B. Pang, and W. Daelemans, Doha, Qatar, October 25-29, 2014, ACL, pp. 1724–1734, 2014. DOI: https://doi.org/10.3115/v1/d14-1179.
Search in Google Scholar Back to article
J. Chung, Ç. Gülçehre, K. H. Cho, and Y. Bengio, Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling, in CoRR, Vol. abs/1412.3555, 2014. arXiv: http://arxiv.org/abs/1412.3555.
Search in Google Scholar Back to article
B. Perozzi, R. Al-Rfou, and S. Skiena, Deep-Walk: Online Learning of Social Representations, in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’14), ACM, New York, NY, USA, August 24-27, 2014, pp. 701–710, 2014. DOI: https://doi.org/10.1145/2623330.2623732.
Search in Google Scholar Back to article
A. Grover and J. Leskovec, node2vec: Scalable Feature Learning for Networks, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16), Assoc Comp Machinery, San Francisco, CA, USA, August 13-17, 2016, pp. 855–864, 2016. DOI: https://doi.org/10.1145/2939672.2939754.
Search in Google Scholar Back to article
A. Narayanan, M. Chandramohan, R. Venkatesan, L. Chen, Y. Liu, and S. Jaiswal, graph2vec: Learning Distributed Representations of Graphs, in arXiv preprint, arXiv:1707.05005 [cs.AI], 2017. DOI: https://doi.org/10.48550/arXiv.1707.05005.
Search in Google Scholar Back to article
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, LINE: Large-scale Information Network Embedding, in Proceedings of the 24th International Conference on World Wide Web (WWW 2015), ACM, Florence, Italy, May 18-22, 2015, pp. 1067–1077, 2015. DOI: https://doi.org/10.1145/2736277.2741093.
Search in Google Scholar Back to article
A. Sperduti and A. Starita, Supervised neural networks for the classification of structures, in IEEE Trans. Neural Networks, Vol. 8, No. 3, pp. 714–735, 1997. DOI: https://doi.org/10.1109/72.572108.
Search in Google Scholar Back to article
M. Gori, G. Monfardini, and F. Scarselli, A new model for learning in graph domains, in IEEE International Joint Conference on Neural Networks (IJCNN 2005), Montreal, QC, Canada, July 31 -August 4, 2005, IEEE, pp. 729–734, 2005. DOI: https://doi.org/10.1109/IJCNN.2005.1555942.
Search in Google Scholar Back to article
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun, Spectral networks and locally connected networks on graphs, in arXiv preprint, arXiv:1312.6203, 2013. URL: https://arxiv.org/pdf/1312.6203.
Search in Google Scholar Back to article
M. Defferrard, X. Bresson, and P. Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, in Advances in Neural Information Processing Systems 29 (NeurIPS 2016), edited by D. D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon, and R. Garnett, Barcelona, Spain, December 5-10, 2016, pp. 3837–3845, 2016. URL: https://proceedings.neurips.cc/paper/2016/hash/04df4d434d481c5bb723be1b6df1ee65-Abstract.html.
Search in Google Scholar Back to article
Y. Li, R. Yu, C. Shahabi, and Y. Liu, Diffusion convolutional recurrent neural network: Data-driven traffic forecasting, in arXiv preprint, arXiv:1707.01926, 2017. DOI: https://doi.org/10.48550/arXiv.1707.01926.
Search in Google Scholar Back to article
B. Yu, H. Yin, and Z. Zhu, Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting, in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI 2018), edited by J. Lang, Stockholm, Sweden, July 13-19, 2018, pp. 3634–3640, 2018.
Search in Google Scholar Back to article
S. Guo, Y. Lin, N. Feng, C. Song, and H. Wan, Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting, in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019), AAAI, Honolulu, HI, USA, January 27 - February 1, 2019, pp. 922–929, 2019.
Search in Google Scholar Back to article
C. Song, Y. Lin, S. Guo, and H. Wan, Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting, in Thirty-Fourth AAAI Conference on Artificial Intelligence, the Thirty-Second Innovative Applications of Artificial Intelligence Conference and the Tenth AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI 2020), New York, NY, USA, February 7 - 12, 2020, Association for the Advancement of Artificial Intelligence (AAAI), pp. 914–921, 2020.
Search in Google Scholar Back to article
L. Bai, L. Yao, S. S. Kanhere, X. Wang, and Q. Z. Sheng, STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting, in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao, Peoples R China, August 10 - 16, 2019, International Joint Conference on Artificial Intelligence (IJCAI), pp. 1981–1987, 2019.
Search in Google Scholar Back to article
Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, Graph WaveNet for Deep Spatial-Temporal Graph Modeling, in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao, Peoples R China, August 10 - 16, 2019, International Joint Conference on Artificial Intelligence (IJCAI), pp. 1907–1913, 2019.
Search in Google Scholar Back to article
A. Pareja, G. Domeniconi, J. Chen, T. Ma, T. Suzumura, H. Kanezashi, T. Kaler, T. B. Schardl, and C. E. Leiserson, EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs, in Thirty-Fourth AAAI Conference on Artificial Intelligence, the Thirty-Second Innovative Applications of Artificial Intelligence Conference and the Tenth AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI 2020), New York, NY, USA, February 7 - 12, 2020, Association for the Advancement of Artificial Intelligence (AAAI), pp. 5363–5370, 2020.
Search in Google Scholar Back to article
J. Li, Y. Liu, and L. Zou, DynGCN: A Dynamic Graph Convolutional Network Based on Spatial-Temporal Modeling, in Web Information Systems Engineering, WISE 2020, Pt I (Lecture Notes in Computer Science, Vol. 12342), Virtual Conference, October 20 - 24, 2020, Springer, pp. 83–95, 2020. DOI: https://doi.org/10.1007/978-3-030-62005-9_7.
Search in Google Scholar Back to article
R. Ying, J. You, C. Morris, X. Ren, W. L. Hamilton, and J. Leskovec, Hierarchical Graph Representation Learning with Differentiable Pooling, in Advances in Neural Information Processing Systems 31 (NIPS 2018) (Advances in Neural Information Processing Systems, Vol. 31), Montreal, QC, Canada, December 2 - 8, 2018, Curran Associates, Inc., 2018
Search in Google Scholar Back to article
L. van der Maaten and G. Hinton, Visualizing Data using t-SNE, Journal of Machine Learning Research, Vol. 9, pp. 2579–2605, November 2008.
Search in Google Scholar Back to article

Phishing Fraud Identity Inference Based on Graph Gated Recurrent Neural Network

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