Detecting Malicious Ethereum Entities via Application of Machine Learning Classification

Poursafaei, Farimah; Hamad, Ghaith Bany; Žilić, Željko

doi:10.1109/brains49436.2020.9223304

Cited by 27 publications

(14 citation statements)

References 28 publications

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“…This article evaluates the ensemble learning method for detecting anomalous or criminal transactions in blockchain networks. Ensemble learning gave good results and great performance in the experiments for recognizing malicious Ethereum entities [73]. Moreover, the authors execute ensemble learning, a mixture of ML predictors that wins over other classical learning approaches at predicting licit and illegitimate transactions.…”

Section: Ensemble Anomaly Detection In Blockchainmentioning

confidence: 99%

“…In addition, there is one research paper that utilized the stacking with boosting strategy and one paper that used the bagging with the voting approach. The authors [73] offered strategies for detecting malicious entities that employ versions of RF, SVM, LR, and ensemble methods with stacking and boosting (AdaBoost Classifier). With an average F1 score of 0.996, the study's findings demonstrate that the ensemble technique yields effective outcomes.…”

Section: B An Anomaly In the Aspect Of Securitymentioning

confidence: 99%

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Combining Multiple Classifiers using Ensemble Method for Anomaly Detection in Blockchain Networks: A Comprehensive Review

Hisham¹,

Makhtar²,

Aziz³

2022

IJACSA

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Blockchain is one of the most anticipated technology revolutions, with immense promise in various applications. It is a distributed and encrypted database that can address a range of challenges connected to online security and trust. While many people identify Blockchain with cryptocurrencies such as Bitcoin, it has a wide range of applications in supply chain management, health, Internet of Things (IoT), education, identity theft prevention, logistics, and the execution of digital smart contracts. Although Blockchain Technology (BT) has numerous advantages for Decentralized Applications (DApps), it is nevertheless vulnerable to abuse, smart contract failures, security, theft, trespassing, and other concerns. As a result, using Machine Learning (ML) models to detect anomalies is an excellent way to detect and safeguard blockchain networks from criminal activity. Adapting ensemble learning methods in ML to create better prediction outcomes is a viable approach for anomaly identification. Ensemble learning, as the name implies, refers to creating a stronger and more accurate classification by combining the prediction results of numerous weak models. As a result, an in-depth evaluation of ensemble learning methodologies for anomaly detection in the blockchain network ecosystem is applied in this paper. It comprises numerous ensemble methods (e.g., averaging, voting, stacking, boosting, bagging). The review collects data from three established databases, which are Scopus, Web of Science (WoS), and Google Scholar. Specific keywords are employed, such as Blockchain, Ethereum, Bitcoin, Anomaly Detection, and Ensemble Learning, employing advanced searching algorithms. The results of the search found 60 primary articles from 2017 to 2022 (30 from Scopus, 20 from the WoS, and 10 from Google Scholar). Based on these findings, we decided to divide our debate into three primary themes: (1) the fundamentals of Blockchain Technology (BT), (2) the overview of ensemble learning, and (3) the integration and analysis of ensemble learning in blockchain networks for anomaly detection. In terms of awareness and knowledge, the results are also discussed in terms of what they mean and where future research should go.

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Section: Ensemble Anomaly Detection In Blockchainmentioning

confidence: 99%

Section: B An Anomaly In the Aspect Of Securitymentioning

confidence: 99%

Combining Multiple Classifiers using Ensemble Method for Anomaly Detection in Blockchain Networks: A Comprehensive Review

Hisham¹,

Makhtar²,

Aziz³

2022

IJACSA

View full text Add to dashboard Cite

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“…Poursafaei F. et al [28] proposed a model to detect malicious entities in Ethereum network, applying Random Forest, Logistic Regression, AdaBoost, and Support Vector Machine classification methods with F1-score of 99%.…”

Section: Related Workmentioning

confidence: 99%

Fraudulent Account Detection in the Ethereum’s Network Using Various Machine Learning Techniques

Sallam¹,

Rassem²,

Abdu³

et al. 2022

IJSECS

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On the Ethereum network, users communicate with one another through a variety of different accounts. Pseudo-anonymity was enforced over the network to provide the highest level of privacy. By using accounts that engage in fraudulent activity across the network, such privacy may be exploited. Like other cryptocurrencies, Ethereum blockchain may exploited with several fraudulent activities such as Ponzi schemes, phishing, or Initial Coin Offering (ICO) exits, etc. However, the identification of parameters with abnormal account characteristics is not an easy task and requires an intelligent approach to distinguish between normal and fraudulent activities. Therefore, this paper has attempted to solve this a problem by using machine learning techniques to introduce a robust approach that can detect fraudulent accounts on Ethereum. We have used a K-Nearest Neighbor, Random Forest and XGBoost over a collected dataset of 4,681 instances along with 2,179 fraudulent accounts associated and 2,502 regular accounts. The XGBoost, RF, and KNN techniques achieved average accuracies of 96.80 %, 94.8 8%, and 87.85% and an average AUC of 0.995, 0.99 and 0.93, respectively.

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“…Poursafaei et al [66], the authors proposed a framework to identify the malicious entities in Ethereum. The proposed framework includes an efficient approach for extracting the features from Ethereum blockchain data that can be used to define entity transactional behavior.…”

Section: F Malicious Account/entities 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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Detecting Malicious Ethereum Entities via Application of Machine Learning Classification

Cited by 27 publications

References 28 publications

Combining Multiple Classifiers using Ensemble Method for Anomaly Detection in Blockchain Networks: A Comprehensive Review

Combining Multiple Classifiers using Ensemble Method for Anomaly Detection in Blockchain Networks: A Comprehensive Review

Fraudulent Account Detection in the Ethereum’s Network Using Various Machine Learning Techniques

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

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