Efficient blockchain addresses classification through cascading ensemble learning approach

Saxena, Rohit; Arora, Deepak; Nagar, Vishal

doi:10.1504/ijesdf.2023.129278

Cited by 6 publications

(3 citation statements)

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“…The characteristics of the P2PKH-type transactions generated by each covert communication scheme are presented in Tables 4 and 5. The static-address correlation and chain-address correlation have been discussed in Section 4.1.2, which make the chan-nel susceptible to address clustering analysis attacks [27]. We set the transaction fee as 0.0000747 BTC/KB acccording to block 801,087 [28], and the exchange rate between Bitcoin and the US Dollar is set as 1:29,325.…”

Section: Capability Comparisonmentioning

confidence: 99%

A Blockchain-Enabled Group Covert Channel against Transaction Forgery

Shen,

Zhu,

Gao

et al. 2024

Mathematics

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As a decentralized network infrastructure, the data sent to the blockchain are public and temper-evident. The cover of massive normal transactions in a blockchain network is ideal for constructing a stable and reliable covert channel to achieve one-to-many group covert communication. Existing blockchain-based covert communication schemes face challenges in balancing concealment, embedding rate and filtering efficiency, making them unsuitable for direct extension to group scenarios. Adopting a key-leakage scheme can increase the channel capacity while maintaining high concealment from external adversaries. However, it will also expose more knowledge to the receiver. A malicious receiver has the ability to steal a sender’s identity or replay historical transactions to control the entire channel. In this paper, we define the capabilities of malicious receivers in blockchain-based group covert communication scenarios and propose a group covert communication scheme resistant to transaction forgery attacks. Theoretical analysis and experiments prove that our covert transactions do not have any transaction correlativity, ensuring the unique authenticity of the sender’s identity while maintaining supreme concealment compared with the existing schemes. The precision and recall of machine learning detection results can reach 0.57–0.62 (0.5 is the ideal value).

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Section: Capability Comparisonmentioning

confidence: 99%

A Blockchain-Enabled Group Covert Channel against Transaction Forgery

Shen,

Zhu,

Gao

et al. 2024

Mathematics

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“…In the Bitcoin blockchain dataset, authors [26] performed the classification and prediction of the proportion of user activities that are lawful and unlawful. Approximately 27 billion samples, separated into nine user behaviours, five of which were unlawful while the other four were lawful, made up the dataset.…”

Section: Blockchain and Cryptocurrenciesmentioning

confidence: 99%

Supervised Machine Learning Framework for Deanonymization of Transactional Entities in Decentralized Infrastructure Environment

Saxena,

Arora,

Nagar

2023

Preprint

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A public ledger is used by Bitcoin, a digital currency, to keep track of transactions. The owner of the Bitcoin keeps their identity secret and is identified only by their unique address. This indicates that because Bitcoin offers anonymity, it may be utilized for illicit purposes on a regular basis. This study presents a supervised machine learning approach for predicting anonymous user activities on the Bitcoin Blockchain. As a training dataset to facilitate the user activities classification, we created a labelled dataset with over 4 million samples from exchanges, gambling, pools, and services whose identities and types were disclosed. The primary goal is to classify transactions on the blockchain in order to deanonymize them and distinguish between legitimate and illegitimate ones. On the class imbalanced dataset, we obtained impressive cross-validation (CV) accuracy using the Gradient Boosting, Random Forest, and eXtreme Gradient Boosting with default parameters and hyperparameters. Using Random Forest helped achieve the best cross-validation accuracy on default parameters and hyperparameters obtained using grid search on the class-balanced dataset using the Synthetic Minority Oversampling Technique, while Bagging and eXtreme Gradient Boosting were used on hyperparameters obtained using randomized search. Empirical results show that the recommended model is up to 98% accurate.

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“…When contrasted to a single decision-maker, the ensemble learning technique is analogous to the decision-making process of numerous decision-makers working in conjunction with one another [52,53]. The weighted majority voting is a kind of ensemble learning, which is realized using a linear combination of the voting results of multiple base classifiers.…”

Section: Feature Selection Decisionmentioning

confidence: 99%

Effective and Interpretable Rule Mining for Dynamic Job-Shop Scheduling via Improved Gene Expression Programming with Feature Selection

Sitahong,

Yuan,

et al. 2023

Applied Sciences

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Gene expression programming (GEP) is frequently used to create intelligent dispatching rules for job-shop scheduling. The proper selection of the terminal set is a critical factor for the success of GEP. However, there are various job features and machine features that can be included in the terminal sets to capture the different characteristics of the job-shop state. Moreover, the importance of features in the terminal set varies greatly between scenarios. The irrelevant and redundant features may lead to high computational requirements and increased difficulty in interpreting generated rules. Consequently, a feature selection approach for evolving dispatching rules with improved GEP has been proposed, so as to select the proper terminal set for different dynamic job-shop scenarios. First, the adaptive variable neighborhood search algorithm was embedded into the GEP to obtain a diverse set of good rules for job-shop scenarios. Secondly, based on the fitness of the good rules and the contribution of features to the rules, a weighted voting ranking method was used to select features from the terminal set. The proposed approach was then compared with GEP-based algorithms and benchmark rules in the different job-shop conditions and scheduling objectives. The experimentally obtained results illustrated that the performance of the dispatching rules generated using the improved GEP algorithm after the feature selection process was better than that of both the baseline dispatching rules and the baseline GEP algorithm.

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Efficient blockchain addresses classification through cascading ensemble learning approach

Cited by 6 publications

References 0 publications

A Blockchain-Enabled Group Covert Channel against Transaction Forgery

A Blockchain-Enabled Group Covert Channel against Transaction Forgery

Supervised Machine Learning Framework for Deanonymization of Transactional Entities in Decentralized Infrastructure Environment

Effective and Interpretable Rule Mining for Dynamic Job-Shop Scheduling via Improved Gene Expression Programming with Feature Selection

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