用主题模型分析云安全日志，提前发现漏洞
Enhancing Cloud Security through Topic Modelling
- 用LDA和PLSA从日志中提取安全主题
- 识别出构建、测试、部署阶段的潜在威胁模式
- 适合关注CI/CD安全的开发者与运维人员
在安全威胁日益复杂持久的背景下，保护云应用至关重要。持续集成与持续部署（CI/CD）流水线尤为脆弱，亟需创新的安全方法。本研究探索自然语言处理技术中的主题建模，用于分析安全相关文本数据并预测潜在威胁。聚焦于隐含狄利克雷分布（LDA）和概率潜在语义分析（PLSA），利用Python的Gensim框架对日志、报告和部署轨迹等数据源进行分析，将日志条目分类为安全相关主题（如钓鱼攻击、加密失败）。这些主题被用于揭示CI/CD各连续阶段（构建、测试、部署）中的安全问题模式。该方法引入语义层，支持早期漏洞识别与运行时行为的上下文理解。
原文摘要 · Abstract (English)
Protecting cloud applications is critical in an era where security threats are increasingly sophisticated and persistent. Continuous Integration and Continuous Deployment (CI/CD) pipelines are particularly vulnerable, making innovative security approaches essential. This research explores the application of Natural Language Processing (NLP) techniques, specifically Topic Modelling, to analyse security-related text data and anticipate potential threats. We focus on Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) to extract meaningful patterns from data sources, including logs, reports, and deployment traces. Using the Gensim framework in Python, these methods categorise log entries into security-relevant topics (e.g., phishing, encryption failures). The identified topics are leveraged to highlight patterns indicative of security issues across CI/CD's continuous stages (build, test, deploy). This approach introduces a semantic layer that supports early vulnerability recognition and contextual understanding of runtime behaviours.
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