网络安全管理域名威胁原始情报数据

1.安全管理平台/态势感知： 多源情报汇聚：域名威胁原始情报数据能够收集来自多个渠道的域名相关信息，如DNS查询记录、域名注册信息、网络流量数据等，实现多源情报的汇聚与整合。 提升情报丰富性：通过整合多源情报，域名威胁原始情报数据能够为安全管理平台提供更为丰富、全面的域名数据，帮助安全团队更深入地了解网络中的域名活动情况。 实时态势感知：基于域名威胁原始情报数据，安全管理平台能够实时监控网络中的域名活动，发现异常域名行为，如恶意域名解析、域名劫持等，为安全团队提供及时的告警和响应支持。 2.威胁情报（TI）： 恶意域名识别：域名威胁原始情报数据包含大量已知的恶意域名信息，通过与这些信息进行比对分析，威胁情报系统能够迅速识别出网络中的恶意域名，为安全团队提供重要的威胁线索。 情报分析与关联：利用域名威胁原始情报数据中的丰富数据，威胁情报系统可以对各种安全事件进行深度分析和关联，发现隐藏在复杂网络环境中的潜在威胁，如钓鱼攻击、勒索软件传播等。 情报共享与协作：域名威胁原始情报数据不仅服务于内部安全团队，还可以与其他组织或机构进行情报共享，共同提升网络安全防护能力，实现更广泛的威胁情报协作。数据采集：通过事件采集系统获取网络安全攻击相关事件，内容包括威胁大类、威胁子类、攻击时间、域名等。通过恶意文件监控系统获取内外部流行恶意文件样本。并综合收集各类产品日志、SaaS监测数据、SaaS防护数据、狩猎数据、资产测绘数据、APT研究、漏洞研究攻防研究、数据安全研究、应用安全研究、物联网安全研究、云安全研究、开源情报、商业情报等
数据清洗：对采集到的数据进行结构化转换和标准化处理，清洗不必要的字段，并进行多维信息的聚合。以更好地满足后续对域名威胁原始情报进行数据分析和生产的需求。
数据加工：
1. 利用AI大模型、LSTM+CRF算法等技术进行自然语言处理和安全文章分析，提取分析相关域名威胁原始情报信息。
2. 采用多因子AI威胁评分模型，对恶意域名的威胁程度进行评估域名域名威胁原始情报风险等级。
3. 采用了创新且前沿的多维度分析方法。通过综合考虑情报来源的历史可靠性、当前情报与已知威胁情报的匹配度、情报内容的详细程度以及与其他独立来源的交叉验证，最终计算得出情报的可信度等级。
4. 利用威胁信息治理模型，结合多源情报及关联数据，进行域名威胁原始情报生产输出

1. Security Management Platform / Situation Awareness:
- Multi-source Intelligence Convergence: Original domain threat intelligence data can collect domain-related information from multiple channels, such as DNS query records, domain name registration information, and network traffic data, to realize the convergence and integration of multi-source intelligence.
- Enhancing Intelligence Richness: By integrating multi-source intelligence, original domain threat intelligence data can provide richer and more comprehensive domain data for the security management platform, helping security teams gain a deeper understanding of domain name activities in the network.
- Real-time Situation Awareness: Based on original domain threat intelligence data, the security management platform can monitor domain name activities in the network in real time, detect abnormal domain name behaviors such as malicious domain name resolution and domain hijacking, and provide timely alert and response support for security teams.

2. Threat Intelligence (TI):
- Malicious Domain Identification: Original domain threat intelligence data contains a large amount of known malicious domain name information. By comparing and analyzing with this information, the threat intelligence system can quickly identify malicious domain names in the network, providing important threat clues for security teams.
- Intelligence Analysis and Correlation: Using the rich data in original domain threat intelligence data, the threat intelligence system can conduct in-depth analysis and correlation of various security incidents, and discover potential threats hidden in complex network environments, such as phishing attacks and ransomware propagation.
- Intelligence Sharing and Collaboration: Original domain threat intelligence data not only serves the internal security team, but can also share intelligence with other organizations or institutions to jointly improve network security protection capabilities and achieve broader threat intelligence collaboration.

Data Collection: Obtain network security attack-related events through an event collection system, including major threat categories, threat subcategories, attack time, domain names, etc. Obtain internal and external popular malicious file samples through a malicious file monitoring system. Also comprehensively collect various product logs, SaaS monitoring data, SaaS protection data, hunting data, asset mapping data, APT research, vulnerability research and offensive-defensive research, data security research, application security research, IoT security research, cloud security research, open source intelligence, and commercial intelligence.

Data Cleaning: Perform structured conversion and standardization processing on the collected data, clean unnecessary fields, and aggregate multi-dimensional information, so as to better meet the needs of subsequent data analysis and production of original domain threat intelligence.

Data Processing:
1. Use technologies such as AI Large Language Models (LLMs) and LSTM+CRF algorithms for natural language processing and security article analysis, and extract and analyze relevant original domain threat intelligence information.
2. Adopt a multi-factor AI threat scoring model to evaluate the threat level of malicious domain names and determine the risk level of original domain threat intelligence data.
3. Adopt an innovative and cutting-edge multi-dimensional analysis method. By comprehensively considering the historical reliability of intelligence sources, the matching degree between current intelligence and known threat intelligence, the detail level of intelligence content, and cross-validation with other independent sources, the credibility level of the intelligence is finally calculated.
4. Use a threat information governance model, combined with multi-source intelligence and associated data, to produce and output original domain threat intelligence.

搜集汇总

数据集介绍

特点

该数据集为网络安全管理域名威胁原始情报数据，包含501条记录，每日更新，主要应用于安全管理平台和威胁情报系统。数据通过多源情报汇聚、实时态势感知、恶意域名识别等方式，提升网络安全防护能力。