用机器学习检测以太坊链上非法内容，发现大量敏感信息。
Detection and Analysis of Sensitive and Illegal Content on the Ethereum Blockchain Using Machine Learning Techniques
- 通过恢复文件、图像和文本，构建数据识别算法
- 情感分析准确率达0.9，识别出15,810条负面文本
- 可检测色情图片并发现针对中国官员的敏感内容
区块链技术因其透明与不可篡改性受到赞誉，但其去中心化结构也带来恶意或非法内容存在的风险。本研究聚焦以太坊，提出一种数据识别与恢复算法，成功恢复175个常见文件、296张图片和91,206条文本。采用FastText算法进行情感分析，经参数调优后准确率达0.9。分类结果显示：70,189条中性、5,208条正面、15,810条负面文本，有助于识别敏感或非法信息。借助NSFWJS库，对7张不雅图像实现100%准确检测。研究揭示以太坊链上共存着良性与有害内容，包括个人数据、色情图像、煽动性语言及种族歧视。值得注意的是，部分敏感信息明确指向中国官员。研究提出预防措施，为公众理解区块链技术及监管机构提供参考。所用算法为解决区块链数据隐私与安全问题提供创新方案。
原文摘要 · Abstract (English)
Blockchain technology, lauded for its transparent and immutable nature, introduces a novel trust model. However, its decentralized structure raises concerns about potential inclusion of malicious or illegal content. This study focuses on Ethereum, presenting a data identification and restoration algorithm. Successfully recovering 175 common files, 296 images, and 91,206 texts, we employed the FastText algorithm for sentiment analysis, achieving a 0.9 accuracy after parameter tuning. Classification revealed 70,189 neutral, 5,208 positive, and 15,810 negative texts, aiding in identifying sensitive or illicit information. Leveraging the NSFWJS library, we detected seven indecent images with 100% accuracy. Our findings expose the coexistence of benign and harmful content on the Ethereum blockchain, including personal data, explicit images, divisive language, and racial discrimination. Notably, sensitive information targeted Chinese government officials. Proposing preventative measures, our study offers valuable insights for public comprehension of blockchain technology and regulatory agency guidance. The algorithms employed present innovative solutions to address blockchain data privacy and security concerns.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。