A Survey on Blockchain Anomaly Detection Using Data Mining Techniques

Li, Ji; Gu, Chunxiang; Wei, Fushan; Chen, Xi

doi:10.1007/978-981-15-2777-7_40

Cited by 12 publications

(9 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, the existing literature is also relatively scarce in terms of comparative studies of different feature dimension reduction and data augmentation methods and their technical effectiveness in anomaly detection based on high-dimensional and sparse graph features in the blockchain. ✓ ✓ ✓ Liang et al [15] ✓ ✓ Ashfaq et al [16] ✓ ✓ ✓ Sanjalawe et al [17] ✓ ✓ ✓ Muhammad et al [18] ✓ ✓ Alarab et al [19] ✓ ✓ ✓ Sharma et al [20] ✓ ✓ ✓ Chen et al [21] ✓ ✓ ✓ Pourhabibi et al [22] ✓ Xiao et al [23] ✓ ✓ ✓ Liu et al [24] ✓ ✓ ✓ Alarab et al [25] ✓ ✓ Elbaghdadi et al [26] ✓ ✓ Nerurkar et al [27] ✓ ✓ Mohammed et al [28] ✓ ✓ Voronov et al [29] ✓ ✓ Venkatesan et al [30] ✓ ✓…”

Section: Challenges and Proposed Methodsmentioning

confidence: 99%

“…In a abnormal transaction cryptocurrency detection method with spatiotemporal and global representation, the CTDM combines EvolveGCN with MGU and global representations to achieve better performance (Xiao et al [23]). An extensive survey of the blockchain anomaly transaction detection was conducted, and graph convolutional networks were applied to the domain of blockchain anomaly detection (Liu et al [24]).…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

An Abnormal Account Identification Method by Topology Feature Analysis for Blockchain-Based Transaction Network

Yue,

Zhang,

Zhang

et al. 2024

Electronics

View full text Add to dashboard Cite

Cryptocurrency, as one of the most successful applications of blockchain technology, has played a vital role in promoting the development of the digital economy. However, its anonymity, large scale of cryptographic transactions, and decentralization have also brought new challenges in identifying abnormal accounts and preventing abnormal transaction behaviors, such as money laundering, extortion, and market manipulation. Recently, some researchers have proposed efficient and accurate abnormal transaction detection based on machine learning. However, in reality, abnormal accounts and transactions are far less common than normal accounts and transactions, so it is difficult for the previous methods to detect abnormal accounts by training with such an imbalance in abnormal/normal accounts. To address the issues, in this paper, we propose a method for identifying abnormal accounts using topology analysis of cryptographic transactions. We consider the accounts and transactions in the blockchain as graph nodes and edges. Since the abnormal accounts may have special topology features, we extract topology features from the transaction graph. By analyzing the topology features of transactions, we discover that the high-dimensional sparse topology features can be compressed by using the singular value decomposition method for feature dimension reduction. Subsequently, we use the generative adversarial network to generate samples like abnormal accounts, which will be sent to the training dataset to produce an equilibrium of abnormal/normal accounts. Finally, we utilize several machine learning techniques to detect abnormal accounts in the blockchain. Our experimental results demonstrate that our method significantly improves the accuracy and recall rate for detecting abnormal accounts in blockchain compared with the state-of-the-art methods.

show abstract

Section: Challenges and Proposed Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An Abnormal Account Identification Method by Topology Feature Analysis for Blockchain-Based Transaction Network

Yue,

Zhang,

Zhang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…According to [ 11 ], fraudulent activities are data mining issues because the central server for credit card transactions tells whether a trading transaction is fake or legal. Fraud detection is not a new problem; yet, there are still numerous challenges.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism

Ashfaq

Khalid

Yahaya

et al. 2022

Sensors

View full text Add to dashboard Cite

In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. However, along with these advanced technologies, many frauds are also increasing every year. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms—XGboost and random forest (RF)—used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. We also calculate the precision and AUC of the models to measure the accuracy. A security analysis of the proposed smart contract is also performed to show the robustness of our system. In addition, an attacker model is also proposed to protect the proposed system from attacks and vulnerabilities.

show abstract

“…Cryptocurrencies are based on using blockchain technologies, each one from a different point of view but with the same goal, i.e., to make transactions safer for users. In that sense, blockchain harbors a multitude of attributes encompassing fault tolerance, resistance to tampering, and the cloak of anonymity [25,26]. However, although blockchain has been acknowledged as the spearhead in developing secure decentralized applications, it has shown certain failures regarding security flaws and transparency in cryptocurrencies and smart contracts [1,27,28].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey

Cholevas,

Angeli,

Sereti

et al. 2024

Algorithms

View full text Add to dashboard Cite

In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal behaviors by examining avant-garde algorithms to discern deviations from normal patterns. By seamlessly blending technological acumen with a discerning gaze, this survey offers a perspective on the symbiotic relationship between unsupervised learning and anomaly detection by reviewing this problem with a categorization of algorithms that are applied to a variety of problems in this field. We propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed not only as an implementation procedure but also as an integration procedure, where the merits of these algorithms can effectively be combined in ways determined by the problem at hand. In that sense, the main contribution of this paper is a thorough study of the interplay between various unsupervised learning algorithms and how this can be used in facing malicious activities and behaviors within public and private blockchain networks. The result is the definition of three categories, the characteristics of which are recognized in terms of the way the respective integration takes place. When implementing unsupervised learning, the structure of the data plays a pivotal role. Therefore, this paper also provides an in-depth presentation of the data structures commonly used in unsupervised learning-based blockchain anomaly detection. The above analysis is encircled by a presentation of the typical anomalies that have occurred so far along with a description of the general machine learning frameworks developed to deal with them. Finally, the paper spotlights challenges and directions that can serve as a comprehensive compendium for future research efforts.

show abstract

A Survey on Blockchain Anomaly Detection Using Data Mining Techniques

Cited by 12 publications

References 37 publications

An Abnormal Account Identification Method by Topology Feature Analysis for Blockchain-Based Transaction Network

An Abnormal Account Identification Method by Topology Feature Analysis for Blockchain-Based Transaction Network

A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism

Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey

Contact Info

Product

Resources

About