Islam Umar, Alsadhan Abeer Abdullah, Alwageed Hathal Salamah, Al-Atawi Abdullah A, Mehmood Gulzar, Ayadi Manel, Alsenan Shrooq
Computer Science, IQRA National University, Peshawar, Swat Campus, Pakistan.
Department of Computer Science, Applied College, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
PeerJ Comput Sci. 2024 Aug 6;10:e2183. doi: 10.7717/peerj-cs.2183. eCollection 2024.
In the rapidly evolving landscape of modern technology, the convergence of blockchain innovation and machine learning advancements presents unparalleled opportunities to enhance computer forensics. This study introduces SentinelFusion, an ensemble-based machine learning framework designed to bolster secrecy, privacy, and data integrity within blockchain systems. By integrating cutting-edge blockchain security properties with the predictive capabilities of machine learning, SentinelFusion aims to improve the detection and prevention of security breaches and data tampering. Utilizing a comprehensive blockchain-based dataset of various criminal activities, the framework leverages multiple machine learning models, including support vector machines, K-nearest neighbors, naive Bayes, logistic regression, and decision trees, alongside the novel SentinelFusion ensemble model. Extensive evaluation metrics such as accuracy, precision, recall, and 1 score are used to assess model performance. The results demonstrate that SentinelFusion outperforms individual models, achieving an accuracy, precision, recall, and 1 score of 0.99. This study's findings underscore the potential of combining blockchain technology and machine learning to advance computer forensics, providing valuable insights for practitioners and researchers in the field.
在现代技术快速发展的格局中，区块链创新与机器学习进步的融合为加强计算机取证带来了前所未有的机遇。本研究介绍了SentinelFusion，这是一个基于集成的机器学习框架，旨在增强区块链系统中的保密性、隐私性和数据完整性。通过将前沿的区块链安全特性与机器学习的预测能力相结合，SentinelFusion旨在改进对安全漏洞和数据篡改的检测与预防。该框架利用基于区块链的各种犯罪活动综合数据集，借助包括支持向量机、K近邻、朴素贝叶斯、逻辑回归和决策树在内的多个机器学习模型，以及新颖的SentinelFusion集成模型。使用诸如准确率、精确率、召回率和F1分数等广泛的评估指标来评估模型性能。结果表明，SentinelFusion优于单个模型，实现了0.99的准确率、精确率、召回率和F1分数。本研究的结果强调了结合区块链技术和机器学习推进计算机取证的潜力，为该领域的从业者和研究人员提供了有价值的见解。
