用户-代理行为分类的聚合网络活动数据集
Geza Lucz1, Bertalan Forstner1
1Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary.
Data in brief
|February 19, 2025
概括
这项研究从2019-2023年收集了6亿个网络访问请求,为分析用户-代理行为和识别机器人提供了大量数据集. 这些数据可以研究网络流量模式和人工智能模型培训.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 网络分析 网络分析
背景情况:
- 网络访问日志对于了解用户行为和安全威胁至关重要.
- 在长时间内自动收集数据对于跟踪不断变化的在线趋势至关重要.
- 大规模的数据集对于强大的AI模型开发和验证至关重要.
研究的目的:
- 为研究目的提供一个全面的网络访问请求数据集.
- 为了促进基于用户代理的行为和网络流量的演变的研究.
- 支持开发和验证利用和机器人识别策略.
主要方法:
- 在2019年至2023年3月期间,从8000多个域名收集了6亿个网络访问请求.
- 利用自动化数据收集与代升级来增强数据领域.
- 使用MySQL与TokuDB存储引擎和碎形树索引用于数据规范化和可扩展性.
主要成果:
- 一个标准化,可扩展的数据集,涵盖了四年的网络访问请求.
- 适用于研究用户代理行为和识别网络漏洞和机器人的数据.
- 一个独特的资源,用于人工智能培训和跟踪长期的用户代理和IP地址演变.
结论:
- 收集的数据集为网络安全和用户行为研究提供了宝贵的资源.
- 数据集的规模和结构支持高级分析,包括人工智能培训.
- 这些数据为网络流量和用户代理识别的长期动态提供了洞察力.
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