Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding

Wu, Jiajing; Yuan, Qi; Lin, Dan; You, Wei; Chen, Weili; Chen, Chuan; Zheng, Zibin

doi:10.1109/tsmc.2020.3016821

Cited by 182 publications

(90 citation statements)

References 31 publications

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“…Embeddings are usually constructed by network representation learning, i.e., an endto-end training method that automatically transforms a network structure into a low-dimensional space. Early network representation methods include deepwalk [116], node2vec [117], or other customized biased random walks [118]. These methods capture the similarity between nodes as the overlap of neighbor nodes found by a random walk.…”

Section: B Transaction Featuresmentioning

confidence: 99%

“…Network structure-based features, such as centrality [145], neighbors' identities [72], [183], and motifs [170] are proven to have prediction power. Network embedded feature selection methods, when used alone, can also achieve reasonable performance compared to hand-picked features [118], [138], [179]. As for learning algorithms, decision tree-based methods, especially random forests, achieved the highest performance in most of the tasks.…”

Section: Learning Tasks Using Transaction Features Address Identitmentioning

confidence: 99%

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Knowledge Discovery in Cryptocurrency Transactions: A Survey

et al. 2021

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Cryptocurrencies gain trust in users by publicly disclosing the full creation and transaction history. In return, the transaction history faithfully records the whole spectrum of cryptocurrency user behaviors. This article analyzes and summarizes the existing research on knowledge discovery in the cryptocurrency transactions using data mining techniques. Specifically, we classify the existing research into three aspects, i.e., transaction tracings and blockchain address linking, the analyses of collective user behaviors, and the study of individual user behaviors. For each aspect, we present the problems, summarize the methodologies, and discuss major findings in the literature. Furthermore, an enumeration of transaction data parsing and visualization tools and services is also provided. Finally, we outline several gaps and trends for future investigation in this research area.

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Section: B Transaction Featuresmentioning

confidence: 99%

Section: Learning Tasks Using Transaction Features Address Identitmentioning

confidence: 99%

Knowledge Discovery in Cryptocurrency Transactions: A Survey

et al. 2021

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show abstract

“…In the proposed model, the behavior was quantified to 'candidates' suspicion level of emails, 'candidates' capability to differentiate between phishing and non-phishing emails, and the extent of bias in their decision to the recent past emails. In [12], the authors proposed an approach to reveal the phishing accruing on Ethereum through its transaction records' mining processes. They conducted a crawling of phishing addresses of two websites and remodeled the network of transactions.…”

Section: Related Workmentioning

confidence: 99%

URL Links Malicious Classification Towards Autonomous Threat Detection Systems

Al-Smadi

Alsmadi

Wahsheh

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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Cyber threat behaviors can take different forms, approaches, and goals. For threat detection systems, it is essential to monitor URLs known for previous malicious attempts. It is also vital to study attack behaviors for the ultimate goal of designing autonomous threat detection systems. We collected a large dataset of URL links annotated toward that goal, manually as benign or malicious links. Several features are collected about those links related to lexicons and structure. Two classification algorithms were employed to extract the best features to predict whether a link is malicious or benign. We were applied three preprocessing techniques, including handling missing values, dealing with outliers, and categorical variables. For two evaluated classifiers, the results' bias is avoided using proper data splitting methods. The quality of the classifiers is evaluated using classifiers' accuracy. The results indicated that Random Forest continuously reported better accuracy than the Decision Tree classifier, which could predict URL link type, whether malicious or not, with the highest accuracy with 98%.

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“…Vasek and Moore [64] surveyed the presence of Bitcoin scams, including Ponzi schemes, mining scams, scam wallets, and fraudulent exchanges. After that, some other studies have characterized various scams including Ponzi schemes [25,26,33,35,62,63,65], scam Initial Coin Offerings (ICOs) [37,49,53,72], market manipulation of cryptocurrencies [32,33,[41][42][43], blockchain honeypots [61], and phishing scams [57,69,70].…”

Section: Blockchain Scamsmentioning

confidence: 99%

Tracking Counterfeit Cryptocurrency End-to-end

GaoBingyu

WangHaoyu

XiaPengcheng

et al. 2020

Proc. ACM Meas. Anal. Comput. Syst.

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The production of counterfeit money has a long history. It refers to the creation of imitation currency that is produced without the legal sanction of government. With the growth of the cryptocurrency ecosystem, there is expanding evidence that counterfeit cryptocurrency has also appeared. In this paper, we empirically explore the presence of counterfeit cryptocurrencies on Ethereum and measure their impact. By analyzing over 190K ERC-20 tokens (or cryptocurrencies) on Ethereum, we have identified 2, 117 counterfeit tokens that target 94 of the 100 most popular cryptocurrencies. We perform an end-to-end characterization of the counterfeit token ecosystem, including their popularity, creators and holders, fraudulent behaviors and advertising channels. Through this, we have identified two types of scams related to counterfeit tokens and devised techniques to identify such scams. We observe that over 7,104 victims were deceived in these scams, and the overall financial loss sums to a minimum of $ 17 million (74,271.7 ETH). Our findings demonstrate the urgency to identify counterfeit cryptocurrencies and mitigate this threat. CCS Concepts: • Security and privacy → Intrusion/anomaly detection and malware mitigation; Web application security; • Information systems → Web mining.

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Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding

Cited by 182 publications

References 31 publications

Knowledge Discovery in Cryptocurrency Transactions: A Survey

Knowledge Discovery in Cryptocurrency Transactions: A Survey

URL Links Malicious Classification Towards Autonomous Threat Detection Systems

Tracking Counterfeit Cryptocurrency End-to-end

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