基于人工智能的分析:使用机器学习改进事务分析

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ahmed I. Alutaibi
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引用次数: 0

摘要

区块链技术通过提供安全和透明的交易平台,重塑了许多行业。本文深入探讨区块链分析和人工智能(AI)的交集,以推进交易分析。主要目的是加强欺诈检测和提高交易效率。通过全面的文献回顾,我们发现了现有知识的差距,并为我们的研究奠定了基础。我们介绍了一种使用机器学习(ML)算法开发的新型事务混合模型,包括支持向量机(svm)、k近邻(KNNs)和随机森林(RF)。这种交易混合模型旨在通过利用每种算法的优势来加强欺诈检测能力。我们策划了一个包含1000个实例的独特数据集,其中包含交易哈希、区块号、交易费用和gas限制等关键交易特征,并使用二元分类指示欺诈性交易。进行了细致的预处理,包括特征工程和用于训练和测试的数据分割。可视化技术,包括基于海运的图、相关图和小提琴图,阐明了数据集的特征。此外,春季色图相关图增强了对特征关系的理解。可视化呈现预处理前后的交易费用分布,突出数据准备的影响。我们用详细的数学方程介绍了新的交易混合分类器(THC),强调了它对交易欺诈检测的贡献。该分类器使用独占或操作集成SVM, KNN和RF输出,展示了模型开发中的创新。为了评估模型的性能,我们进行了比较分析,结合了支持向量机,KNN, RF和投票分类器。准确度、精密度、召回率和F1分数的条形图,以及自定义的等离子体颜色图,提供了每个模型指标的可视化摘要。此外,提出了接收者工作特征(ROC)曲线分析,突出显示了SVM, KNN, RF和投票模型的曲线下面积(AUC),从而全面了解了它们在区分真阳性率和假阳性率方面的表现。我们提出的方法在欺诈检测方面的有效性超过99%,强调了其在交易分析方面的潜在影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Blockchain Analytics Based on Artificial Intelligence: Using Machine Learning for Improved Transaction Analysis

Blockchain Analytics Based on Artificial Intelligence: Using Machine Learning for Improved Transaction Analysis

Blockchain Analytics Based on Artificial Intelligence: Using Machine Learning for Improved Transaction Analysis

Blockchain Analytics Based on Artificial Intelligence: Using Machine Learning for Improved Transaction Analysis

Blockchain Analytics Based on Artificial Intelligence: Using Machine Learning for Improved Transaction Analysis

Blockchain technology has reshaped numerous industries by providing secure and transparent transactional platforms. This paper delves into the intersection of blockchain analytics and artificial intelligence (AI) to advance transaction analysis. The primary aim is to bolster fraud detection and enhance transaction efficiency. Through a comprehensive literature review, we identify gaps in existing knowledge and lay the groundwork for our research. We introduce a novel transaction-hybrid model developed using machine learning (ML) algorithms, including support vector machines (SVMs), K-nearest neighbors (KNNs), and random forest (RF). This transact-hybrid model aims to fortify fraud detection capabilities by harnessing the strengths of each algorithm. We curate a unique dataset comprising 1000 instances, incorporating critical transaction features such as transaction hash, block number, transaction fee and gas limit, with binary classification indicating fraudulent transactions. Meticulous preprocessing, including feature engineering and data splitting for training and testing, is conducted. Visualization techniques, including seaborn-based graphs, correlation plots and violin plots, elucidate the dataset’s characteristics. Additionally, a spring colormap correlation map enhances the understanding of feature relationships. Transaction fee distributions before and after preprocessing are visually presented, highlighting the impact of data preparation. We introduce the novel transact-hybrid classifier (THC) with detailed mathematical equations, emphasising its contribution to transactional fraud detection. The classifier integrates SVM, KNN and RF outputs using an exclusive OR operation, showcasing innovation in model development. To evaluate model performance, we conduct a comparative analysis, incorporating SVM, KNN, RF and a voting classifier. Bar plots for accuracy, precision, recall and F1 score, with a custom plasma colormap, offer a visual summary of each model’s metrics. Furthermore, a receiver operating characteristics (ROC) curve analysis is presented, highlighting the area under the curve (AUC) for SVM, KNN, RF and voting models, providing a comprehensive view of their performance in distinguishing between true positive and false positive rates. Our proposed method demonstrates over 99% efficacy in fraud detection, underscoring its potential impact in transaction analysis.

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来源期刊
IET Information Security
IET Information Security 工程技术-计算机:理论方法
CiteScore
3.80
自引率
7.10%
发文量
47
审稿时长
8.6 months
期刊介绍: IET Information Security publishes original research papers in the following areas of information security and cryptography. Submitting authors should specify clearly in their covering statement the area into which their paper falls. Scope: Access Control and Database Security Ad-Hoc Network Aspects Anonymity and E-Voting Authentication Block Ciphers and Hash Functions Blockchain, Bitcoin (Technical aspects only) Broadcast Encryption and Traitor Tracing Combinatorial Aspects Covert Channels and Information Flow Critical Infrastructures Cryptanalysis Dependability Digital Rights Management Digital Signature Schemes Digital Steganography Economic Aspects of Information Security Elliptic Curve Cryptography and Number Theory Embedded Systems Aspects Embedded Systems Security and Forensics Financial Cryptography Firewall Security Formal Methods and Security Verification Human Aspects Information Warfare and Survivability Intrusion Detection Java and XML Security Key Distribution Key Management Malware Multi-Party Computation and Threshold Cryptography Peer-to-peer Security PKIs Public-Key and Hybrid Encryption Quantum Cryptography Risks of using Computers Robust Networks Secret Sharing Secure Electronic Commerce Software Obfuscation Stream Ciphers Trust Models Watermarking and Fingerprinting Special Issues. Current Call for Papers: Security on Mobile and IoT devices - https://digital-library.theiet.org/files/IET_IFS_SMID_CFP.pdf
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