Expose Your Mask: Smart Ponzi Schemes Detection on Blockchain

Fan, Shuhui; Fu, Shaojing; Xu, Haoran; Zhu, Chengzhang

doi:10.1109/ijcnn48605.2020.9207143

Cited by 25 publications

(13 citation statements)

References 15 publications

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“…Conclusions/findings 2020 [87] Journal SVM Ethereum IoT T he result is Evaluation Metrics (0.99), Accuracy (0.9998), Recall (1) and F1-Score (0.9998) 2020 [88] Journal Ordered Boosting Ethereum Ponzi Scheme On a real-world dataset, the new model obtains a 98 percent F-score, greatly outperforming existing techniques. 2020 [89] Journal RF Cryptocurrency Cryptojacking On our dataset, the BRENNT DROID tool can detect miners with 95 percent accuracy.…”

Section: Ye Ar Re Fe Rence Type Model Blockchain Applicationmentioning

confidence: 95%

A comprehensive review of significant learning for anomalous transaction detection using a machine learning method in a decentralized blockchain network

2022

IJATEE

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Blockchain technology has changed the global trading of assets. A blockchain can be viewed as a connected ledger managed by a distributed peer-to-peer (P2P) network. Blockchain offers distinctive characteristics such as transactional privacy, the immutability of data, transparency and cryptographic, among others. These features paved the door for blockchain to develop numerous technology solution, including voting applications [1,2], internet of things (IoT) [3,4], and supply chain management (SCM) [5,6], among others. The increasing desire for technological advancements stimulated the development of BT.

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Section: Ye Ar Re Fe Rence Type Model Blockchain Applicationmentioning

confidence: 95%

A comprehensive review of significant learning for anomalous transaction detection using a machine learning method in a decentralized blockchain network

2022

IJATEE

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“…Another interesting work was done by Fan et al [60], the authors proposed a new automated detection approach to detect the Ponzi schemes of smart contracts. The authors combined data mining and machine learning technology to automate their approach.…”

Section: B Ponzi Scheme Detectionmentioning

confidence: 99%

Applicability of Intrusion Detection System on Ethereum Attacks: A Comprehensive Review

et al. 2022

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Ethereum attracts more investors, researchers, and even scammers for many reasons; this is the first platform that enables the new Decentralized Applications (DApps) to run on top of the blockchain network. However, the rich semantics and applications of DApps inevitably introduce many security issues that have grabbed significant attention from industry and academics due to their destructive impact on DApps in recent years. Therefore, there is a vital need to study the applicability of Intrusion Detection System in detecting Ethereum-based attacks. Hence, this paper is among the first comprehensive review that studies the applicability of IDS in detecting Ethereum-based attacks. In addition, this paper lists all the potential attacks on Ethereum passing through the vulnerabilities that cause those attacks and ending with the consequences of each attack. Besides, this paper analyses all the IDS-based related works of Ethereum attacks detection since the Ethereum platform was launched in 2015. Finally, this paper discusses the open issues regarding vulnerabilities and attacks, challenges, and future directions.

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“…Since feature engineering is hard to depict more complex transaction behavior, Chen et al [26] utilized graph embedding to automatically learn high-expressive code features. In order to improve a combination of feature engineering and machine learning, Fan et al [27] trained a Ponzi scheme detection model using the idea of ordered augmentation. Zhang et al [28] proposed a new method for Ethereum Ponzi scheme detection based on an improved LightGBM algorithm with Smote + Tomek, which can alleviate the imbalance problem of Ponzi data.…”

Section: B Graph-based Ponzi Schemes Detection In Ethereummentioning

confidence: 99%

Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum

Jin¹,

Zhou²,

Jin³

et al. 2022

Preprint

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With the development of Web 3.0 which emphasizes decentralization, blockchain technology ushers in its revolution and also brings numerous challenges, particularly in the field of cryptocurrency. Recently, a large number of criminal behaviors continuously emerge on blockchain, such as Ponzi schemes and phishing scams, which severely endanger decentralized finance. Existing graph-based abnormal behavior detection methods on blockchain usually focus on constructing homogeneous transaction graphs without distinguishing the heterogeneity of nodes and edges, resulting in partial loss of transaction pattern information. Although existing heterogeneous modeling methods can depict richer information through metapaths, the extracted metapaths generally neglect temporal dependencies between entities and do not reflect real behavior. In this paper, we introduce Time-aware Metapath Feature Augmentation (TMFAug) as a plug-and-play module to capture the real metapath-based transaction patterns during Ponzi scheme detection on Ethereum. The proposed module can be adaptively combined with existing graph-based Ponzi detection methods. Extensive experimental results show that our TMFAug can help existing Ponzi detection methods achieve significant performance improvements on the Ethereum dataset, indicating the effectiveness of heterogeneous temporal information for Ponzi scheme detection.

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Expose Your Mask: Smart Ponzi Schemes Detection on Blockchain

Cited by 25 publications

References 15 publications

A comprehensive review of significant learning for anomalous transaction detection using a machine learning method in a decentralized blockchain network

A comprehensive review of significant learning for anomalous transaction detection using a machine learning method in a decentralized blockchain network

Applicability of Intrusion Detection System on Ethereum Attacks: A Comprehensive Review

Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum

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