Literature reviews are a critical component of formulating and justifying new research, but are a manual and often time-consuming process. This research introduces a novel, generalizable approach to literature analysis called CEKER which uses a three-step process to streamline the collection of literature, the extraction of key insights, and the summarized analysis of key trends and gaps. Leveraging Large Language Models (LLMs), this methodology represents a significant shift from traditional manual literature reviews, offering a scalable, flexible, and repeatable approach that can be applied across diverse research domains. A case study on unikernel security illustrates CEKER's ability to generate novel insights validated against previous manual methods. CEKER's analysis highlighted reduced attack surface as the most prominent theme. Key security gaps included the absence of Address Space Layout Randomization, missing debugging tools, and limited entropy generation, all of which represent important challenges to unikernel security. The study also revealed a reliance on hypervisors as a potential attack vector and emphasized the need for dynamic security adjustments to address real-time threats.

翻译：文献综述是构思和论证新研究的关键组成部分，但通常是一个手动且耗时的过程。本研究提出了一种新颖、可泛化的文献分析方法，称为CEKER。该方法采用三步流程，以简化文献收集、关键见解提取以及对关键趋势与空白的总结性分析。通过利用大语言模型，此方法代表了从传统手动文献综述的重大转变，提供了一种可扩展、灵活且可重复的途径，能够应用于不同的研究领域。一项关于unikernel安全性的案例研究展示了CEKER生成新颖见解的能力，这些见解已通过先前的手动方法得到验证。CEKER的分析突显了“攻击面减小”是最突出的主题。关键的安全空白包括地址空间布局随机化的缺失、调试工具的缺乏以及有限的熵生成，所有这些都构成了unikernel安全性面临的重要挑战。该研究还揭示了依赖虚拟机监控程序作为潜在攻击向量的问题，并强调需要进行动态安全调整以应对实时威胁。