Abstract
Blockchain has attracted an increasing amount of researches, and there are lots of refreshing implementations in different fields. Cryptocurrency as its representative implementation, suffers the economic loss due to phishing scams. In our work, accounts and transactions are treated as nodes and edges, thus detection of phishing accounts can be modeled as a node classification problem. Correspondingly, we propose a detecting method based on Graph Convolutional Network and autoencoder to precisely distinguish phishing accounts. Experiments on different large-scale real-world datasets from Ethereum show that our proposed model consistently performs promising results compared with related methods.
- Shuichiro Haruta, Hiromu Asahina, and Iwao Sasase. 2017. Visual similarity-based phishing detection scheme using image and CSS with target Website finder. In 2017 IEEE Global Communications Conference, GLOBECOM 2017, Singapore, December 4-8, 2017. IEEE, 1--6. https://doi.org/10.1109/GLOCOM.2017.8254506Google ScholarDigital Library
- Neda Abdelhamid, Aladdin Ayesh, and Fadi A. Thabtah. 2014. Phishing detection-based associative classification data mining. Expert Syst. Appl. 41, 13 (2014), 5948--5959. DOI:https://doi.org/10.1016/j.eswa.2014.03.019Google ScholarCross Ref
- Amr Ahmed, Nino Shervashidze, Shravan M. Narayanamurthy, Vanja Josifovski, and Alexander J. Smola. 2013. Distributed large-scale natural graph factorization. In Proceedings of the 22nd International World Wide Web Conference (WWW’13). 37--48. DOI:https://doi.org/10.1145/2488388.2488393Google Scholar
- Moses Adebowale Akanbi, Khin T. Lwin, E. Sánchez, and M. Alamgir Hossain. 2019. Intelligent web-phishing detection and protection scheme using integrated features of images, frames and text. Expert Syst. Appl. 115 (2019), 300--313. DOI:https://doi.org/10.1016/j.eswa.2018.07.067Google ScholarCross Ref
- Mikhail Belkin and Partha Niyogi. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Proceedings of the Conference on Advances in Neural Information Processing Systems: Natural and Synthetic (NIPS’01). 585--591.Google ScholarDigital Library
- M. Bellingeri, D. Bevacqua, F. Scotognella, and D. Cassi. 2019. The heterogeneity in link weights may decrease the robustness of real-world complex weighted networks. Sci. Rep. 9, 1 (2019), 1--13.Google Scholar
- Stefano Boccaletti, Vito Latora, Yamir Moreno, Martin Chavez, and D.-U. Hwang. 2006. Complex networks: Structure and dynamics. Phys. Rep. 424, 4-5 (2006), 175--308.Google ScholarCross Ref
- Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2015. GraRep: Learning graph representations with global structural information. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, (CIKM’15). 891--900. DOI:https://doi.org/10.1145/2806416.2806512Google ScholarDigital Library
- Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2016. Deep neural networks for learning graph representations. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 1145--1152.Google ScholarCross Ref
- Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R. Hruschka, and Tom M. Mitchell. 2010. Toward an architecture for never-ending language learning. In Proceedings of the 24th AAAI Conference on Artificial Intelligence.Google Scholar
- Weili Chen, Jun Wu, Zibin Zheng, Chuan Chen, and Yuren Zhou. 2019. Market manipulation of bitcoin: Evidence from mining the Mt. Gox transaction network. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’19). 964--972. DOI:https://doi.org/10.1109/INFOCOM.2019.8737364Google ScholarDigital Library
- Weili Chen, Zibin Zheng, Edith Cheuk-Han Ngai, Peilin Zheng, and Yuren Zhou. 2019. Exploiting blockchain data to detect smart ponzi schemes on ethereum. IEEE Access 7 (2019), 37575--37586. DOI:https://doi.org/10.1109/ACCESS.2019.2905769Google ScholarCross Ref
- Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu. 2019. Deep anomaly detection on attributed networks. In Proceedings of the SIAM International Conference on Data Mining. SIAM, 594--602.Google ScholarCross Ref
- Yong Fang, Cheng Zhang, Cheng Huang, Liang Liu, and Yue Yang. 2019. Phishing email detection using improved RCNN model with multilevel vectors and attention mechanism. IEEE Access 7 (2019), 56329--56340. DOI:https://doi.org/10.1109/ACCESS.2019.2913705Google ScholarCross Ref
- Palash Goyal and Emilio Ferrara. 2018. Graph embedding techniques, applications, and performance: A survey. Knowl.-Based Syst. 151 (2018), 78--94. DOI:https://doi.org/10.1016/j.knosys.2018.03.022Google ScholarCross Ref
- Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 855--864.Google ScholarDigital Library
- John Guare. 1990. Six Degrees of Separation: A Play. Vintage.Google Scholar
- Yuexian Hou, Peng Zhang, Xingxing Xu, Xiaowei Zhang, and Wenjie Li. 2009. Nonlinear dimensionality reduction by locally linear inlaying. IEEE Trans. Neural Netw. 20, 2 (2009), 300--315. DOI:https://doi.org/10.1109/TNN.2008.2005582Google ScholarDigital Library
- Ankit Kumar Jain and B. B. Gupta. 2018. Towards detection of phishing websites on client-side using machine learning-based approach. Telecommun. Syst. 68, 4 (01 Aug 2018), 687--700. DOI:https://doi.org/10.1007/s11235-017-0414-0Google Scholar
- Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems. MIT Press, 3146--3154.Google Scholar
- Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. Retrieved from https://arXiv:1412.6980.Google Scholar
- Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. Retrieved from https://arXiv:1609.02907.Google Scholar
- Thomas N. Kipf and Max Welling. 2016. Variational graph auto-encoders. Retrieved from https://arxiv:1611.07308.Google Scholar
- Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, and Rajeev Rastogi (Eds.). 2016. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM. DOI:https://doi.org/10.1145/2939672Google ScholarDigital Library
- Solomon Kullback and Richard A Leibler. 1951. On information and sufficiency. Ann. Math. Stat. 22, 1 (1951), 79--86.Google ScholarCross Ref
- Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436--444.Google Scholar
- Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI’18), the 30th Innovative Applications of Artificial Intelligence (IAAI’18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI’18). 3538--3545.Google Scholar
- Yukun Li, Zhenguo Yang, Xu Chen, Huaping Yuan, and Wenyin Liu. 2019. A stacking model using URL and HTML features for phishing webpage detection. Future Gen. Comput. Syst. 94 (2019), 27--39. DOI:https://doi.org/10.1016/j.future.2018.11.004Google ScholarDigital Library
- Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le Song. 2018. Heterogeneous graph neural networks for malicious account detection. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM’18). 2077--2085. DOI:https://doi.org/10.1145/3269206.3272010Google ScholarDigital Library
- Jian Mao, Jingdong Bian, Wenqian Tian, Shishi Zhu, Tao Wei, Aili Li, and Zhenkai Liang. 2019. Phishing page detection via learning classifiers from page layout feature. EURASIP J. Wireless Comm. Netw. 2019 (2019), 43. DOI:https://doi.org/10.1186/s13638-019-1361-0Google ScholarCross Ref
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. Retrieved from https://arXiv:1301.3781.Google Scholar
- Satoshi Nakamoto and A. Bitcoin. 2008. A peer-to-peer electronic cash system. Bitcoin. https://bitcoin.org/bitcoin.pdf4.Google Scholar
- Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu. 2016. Asymmetric transitivity preserving graph embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1105--1114. DOI:https://doi.org/10.1145/2939672.2939751Google ScholarDigital Library
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 701--710.Google ScholarDigital Library
- Marc Pilkington. 2016. Blockchain technology: principles and applications. In Research Handbook on Digital Transformations. Edward Elgar Publishing.Google Scholar
- Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. 2018. DeepInf: Social influence prediction with deep learning. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining (KDD’18). 2110--2119. DOI:https://doi.org/10.1145/3219819.3220077Google ScholarDigital Library
- Routhu Srinivasa Rao and Alwyn Roshan Pais. 2019. Jail-Phish: An improved search engine-based phishing detection system. Comput. Secur. 83 (2019), 246--267. DOI:https://doi.org/10.1016/j.cose.2019.02.011Google ScholarDigital Library
- Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2019. The truly deep graph convolutional networks for node classification. Retrieved from https://arxiv:1907.10903.Google Scholar
- Ozgur Koray Sahingoz, Ebubekir Buber, Önder Demir, and Banu Diri. 2019. Machine learning-based phishing detection from URLs. Expert Syst. Appl. 117 (2019), 345--357. DOI:https://doi.org/10.1016/j.eswa.2018.09.029Google ScholarCross Ref
- Nick Szabo. 1997. Formalizing and securing relationships on public networks. First Monday 2, 9 (1997).Google Scholar
- Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. LINE: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web (WWW’15). 1067--1077. DOI:https://doi.org/10.1145/2736277.2741093Google ScholarDigital Library
- Duncan J. Watts and Steven H. Strogatz. 1998. Collective dynamics of ‘small-world’ networks. Nature 393, 6684 (1998), 440.Google Scholar
- Emma J. Williams and Danielle Polage. 2019. How persuasive is phishing email? The role of authentic design, influence and current events in email judgements. Behav. IT 38, 2 (2019), 184--197. DOI:https://doi.org/10.1080/0144929X.2018.1519599Google ScholarCross Ref
- Jiajing Wu, Qi Yuan, Dan Lin, Wei You, Weili Chen, Chuan Chen, and Zibin Zheng. 2019. Who are the phishers? Phishing scam detection on ethereum via network embedding. Retrieved from https://arXiv:1911.09259.Google Scholar
- Guanhua Yan, Stephan Eidenbenz, and Emanuele Galli. 2009. SMS-watchdog: Profiling social behaviors of SMS users for anomaly detection. In Proceedings of the 12th International Symposium on Recent Advances in Intrusion Detection (RAID’09). 202--223. DOI:https://doi.org/10.1007/978-3-642-04342-0_11Google ScholarDigital Library
- Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. 2018. Recent trends in deep learning-based natural language processing. IEEE Comput. Intell. Mag. 13, 3 (2018), 55--75.Google ScholarCross Ref
- Y. Yuan and F. Wang. 2018. Blockchain and cryptocurrencies: Model, techniques, and applications. IEEE Trans. Syst. Man Cybernet.: Syst. 48, 9 (Sep. 2018), 1421--1428. DOI:https://doi.org/10.1109/TSMC.2018.2854904Google ScholarCross Ref
- Xi Zhang, Zhiwei Yan, Hongtao Li, and Guanggang Geng. 2017. Chinese journal of network and information security. Res. Phish. Detect. Technol. 7 (2017).Google Scholar
- Zibin Zheng, Shaoan Xie, Hong-Ning Dai, Xiangping Chen, and Huaimin Wang. 2018. Blockchain challenges and opportunities: A survey. Int. J. Web Grid Services 14, 4 (2018), 352--375. DOI:https://doi.org/10.1504/IJWGS.2018.10016848Google ScholarCross Ref
Index Terms
- Phishing Scams Detection in Ethereum Transaction Network
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