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“颜读（152）：精读期刊论文《开源软件供应链漏洞威胁智能感知》摘要”
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Today, the editor brings the
“Yan Du (152): In-depth reading of the journal paper ‘Intelligent Perception of Vulnerability Threats in the Open Source Software Supply Chain’—Abstract”
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今天小编将从思维导图、精读内容、知识补充三个板块为大家带来《开源软件供应链漏洞威胁智能感知》摘要的介绍。
Today, the editor will introduce the abstract of "Intelligent Perception of Open Source Software Supply Chain Vulnerabilities and Threats" from three sections: mind mapping, intensive reading content, and knowledge supplement.
一、思维导图（Mind Mapping）
二、精读内容（Conduct in-depth reading of the material）
1.研究背景（Research Background）
开源软件的繁荣推动了软件领域的蓬勃发展,也促使以开源软件为基础的供应链开发模式的形成.开源软件供应链本质上是个复杂的供应链拓扑网络,由开源生态的关键元素及其关联关系构成,其产品全球化等优势有助于提高软件行业的开发效率。然而,开源软件供应链也存在依赖关系复杂、传播范围广泛、攻击面暴露扩大等特点,带来了新的安全风险。
The flourishing of open-source software has driven rapid growth in the software sector and fostered the emergence of supply chain development models based on open-source software. Fundamentally, the open-source software supply chain is a complex network topology comprising key elements of the open-source ecosystem and their interrelationships; its advantages—such as globalized product distribution—help enhance development efficiency within the software industry. However, this supply chain model also presents new security risks, characterized by complex dependencies, extensive reach, and an expanded attack surface.
2.面临问题（Challenges Faced）
现有的以安全漏洞、威胁情报为基础的安全管理虽然可以实现安全预警、预先防御,但是由于漏洞威胁信息获取不及时、缺少攻击技术和缓解措施等信息,严重影响了漏洞处理效率。
While existing security management based on security vulnerabilities and threat intelligence enables early warnings and proactive defense, the efficiency of vulnerability remediation is severely hampered by delays in acquiring threat information and a lack of data regarding attack techniques and mitigation measures.
3.核心方法（Core method）
针对上述问题,设计并实现一种针对开源软件供应链的漏洞威胁智能感知方法,包括两部分:1)构建CTI(网络威胁情报)知识图谱,在其构建的过程中使用到相关技术,可以实现安全情报的实时分析与处理,尤其提出SecERNIE模型以及软件包命名矩阵,分别缓解漏洞威胁关联挖掘的问题和开源软件别名的问题。2)漏洞风险信息推送,以软件包命名矩阵为基础,构建软件包过滤规则,实现开源系统漏洞实时过滤与推送。
To address the aforementioned issues, an intelligent vulnerability threat awareness method for the open-source software supply chain is designed and implemented. This method comprises two components: 1) Constructing a Cyber Threat Intelligence (CTI) knowledge graph using relevant technologies to enable real-time analysis and processing of security intelligence; specifically, the SecERNIE model and a software package naming matrix are proposed to mitigate challenges related to vulnerability threat association mining and open-source software aliasing, respectively. 2) Vulnerability risk information dissemination, which involves establishing package filtering rules based on the naming matrix to facilitate the real-time filtering and notification of vulnerabilities in open-source systems.
4.实验成果与成效（Experimental Results and Outcomes）
实验结果显示,相较于NVD等传统漏洞平台,本方法平均感知时间最高提前90.03天;在操作系统软件覆盖率上提升74.37%,并利用SecERNIE模型实现63492个CVE漏洞与攻击技术实体之间的关联关系映射.特别地,针对openEuler操作系统,可追踪的系统软件覆盖率达到92.76%,并累计感知6239个安全漏洞;同时,还发现openEuler中891条漏洞与攻击的关联关系,进而获取到相应的解决方案,为漏洞处理提供了参考依据.在真实攻击环境验证2种典型的攻击场景,证明所提方法在漏洞威胁感知方面的良好的效果。
Experimental results demonstrate that, compared to traditional vulnerability platforms like the NVD, this method advances the average detection time by up to 90.03 days and increases operating system software coverage by 74.37%. Furthermore, the SecERNIE model was utilized to map the associations between 63,492 CVE vulnerabilities and attack technique entities. Specifically, for the openEuler operating system, the traceable software coverage reached 92.76%, with a total of 6,239 security vulnerabilities detected; additionally, 891 associations between vulnerabilities and attacks were identified within openEuler, enabling the retrieval of corresponding solutions and providing a reference for vulnerability remediation. Validation in real-world attack environments across two typical attack scenarios confirms the method's effectiveness in vulnerability threat detection.
三、知识补充（SupplementaryKnowledge）
SecERNIE是一种融合网络安全知识图谱和领域预训练技术的安全领域语言模型。其基本思想是：在ERNIE语言模型的基础上，引入网络安全领域的大规模专业语料、漏洞知识、攻击技术知识以及实体关系知识，使模型能够理解网络安全领域中的复杂语义，提高安全分析和推理能力。相比普通BERT或ERNIE，SecERNIE不仅学习语言文本，还学习安全知识之间的关联关系。
SecERNIE is a language model for the cybersecurity domain that integrates cybersecurity knowledge graphs with domain-specific pre-training techniques. Its core concept involves building upon the ERNIE language model by incorporating large-scale specialized corpora, as well as knowledge regarding vulnerabilities, attack techniques, and entity relationships; this enables the model to comprehend complex semantics within the cybersecurity field and enhances its capabilities in security analysis and reasoning. Unlike standard BERT or ERNIE models, SecERNIE learns not only from textual data but also from the interrelationships between security-related concepts.
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翻译：Google翻译
参考资料：ChatGPT
参考文献：王丽敏,吴敬征,武延军,等.开源软件供应链漏洞威胁智能感知[J].软件学报, 2025, 36(2): 511-536. DOI:10.13328/j.cnki.jos.007163.
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