Attack knowledge graph construction seeks to convert textual cyber threat
intelligence (CTI) reports into structured representations, portraying the
evolutionary traces of cyber attacks. Even though previous research has
proposed various methods to construct attack knowledge graphs, they generally
suffer from limited generalization capability to diverse knowledge types as
well as requirement of expertise in model design and tuning. Addressing these
limitations, we seek to utilize Large Language Models (LLMs), which have
achieved enormous success in a broad range of tasks given exceptional
capabilities in both language understanding and zero-shot task fulfillment.
Thus, we propose a fully automatic LLM-based framework to construct attack
knowledge graphs named: AttacKG+. Our framework consists of four consecutive
modules: rewriter, parser, identifier, and summarizer, each of which is
implemented by instruction prompting and in-context learning empowered by LLMs.
Furthermore, we upgrade the existing attack knowledge schema and propose a
comprehensive version. We represent a cyber attack as a temporally unfolding
event, each temporal step of which encapsulates three layers of representation,
including behavior graph, MITRE TTP labels, and state summary. Extensive
evaluation demonstrates that: 1) our formulation seamlessly satisfies the
information needs in threat event analysis, 2) our construction framework is
effective in faithfully and accurately extracting the information defined by
AttacKG+, and 3) our attack graph directly benefits downstream security
practices such as attack reconstruction. All the code and datasets will be
released upon acceptance.

攻击知识图谱构建：使用大型语言模型和 LLM 框架完全自动化构建攻击知识图谱，包括重写、解析、识别和总结模块，提供关于攻击事件分析所需的信息，并有效地提取 AttacKG + 所定义的信息，从而在攻击重建等下游安全实践中直接受益。

AttacKG+: 使用大型语言模型增强攻击知识图谱构建

AttacKG+:Boosting Attack Knowledge Graph Construction with Large  Language Models

The understanding of large-scale scientific software poses significant
challenges due to its diverse codebase, extensive code length, and target
computing architectures. The emergence of generative AI, specifically large
language models (LLMs), provides novel pathways for understanding such complex
scientific codes. This paper presents S3LLM, an LLM-based framework designed to
enable the examination of source code, code metadata, and summarized
information in conjunction with textual technical reports in an interactive,
conversational manner through a user-friendly interface. S3LLM leverages
open-source LLaMA-2 models to enhance code analysis through the automatic
transformation of natural language queries into domain-specific language (DSL)
queries. Specifically, it translates these queries into Feature Query Language
(FQL), enabling efficient scanning and parsing of entire code repositories. In
addition, S3LLM is equipped to handle diverse metadata types, including DOT,
SQL, and customized formats. Furthermore, S3LLM incorporates retrieval
augmented generation (RAG) and LangChain technologies to directly query
extensive documents. S3LLM demonstrates the potential of using locally deployed
open-source LLMs for the rapid understanding of large-scale scientific
computing software, eliminating the need for extensive coding expertise, and
thereby making the process more efficient and effective. S3LLM is available at
this https URL

利用基于 LLM 的框架进行大规模科学软件的代码分析，通过自然语言查询实现快速理解以及消除对广泛编码经验的需求。

S3LLM: 使用源代码、元数据和文档的大规模科学软件理解

S3LLM: Large-Scale Scientific Software Understanding with LLMs using  Source, Metadata, and Document

Large Language Models (LLMs), typified by OpenAI's GPT series and Meta's
LLaMA variants, have marked a significant advancement in artificial
intelligence. Trained on vast amounts of text data, LLMs are capable of
understanding and generating human-like text across a diverse range of topics.
This study expands on the applications of LLMs, exploring their potential in
data preprocessing, a critical stage in data mining and analytics applications.
We delve into the applicability of state-of-the-art LLMs such as GPT-3.5,
GPT-4, and Vicuna-13B for error detection, data imputation, schema matching,
and entity matching tasks. Alongside showcasing the inherent capabilities of
LLMs, we highlight their limitations, particularly in terms of computational
expense and inefficiency. We propose an LLM-based framework for data
preprocessing, which integrates cutting-edge prompt engineering techniques,
coupled with traditional methods like contextualization and feature selection,
to improve the performance and efficiency of these models. The effectiveness of
LLMs in data preprocessing is evaluated through an experimental study spanning
12 datasets. GPT-4 emerged as a standout, achieving 100\% accuracy or F1 score
on 4 datasets, suggesting LLMs' immense potential in these tasks. Despite
certain limitations, our study underscores the promise of LLMs in this domain
and anticipates future developments to overcome current hurdles.

此研究拓展了大型语言模型（LLMs）的应用，探索了它们在数据预处理中的潜力，包括错误检测、数据插补、模式匹配和实体匹配任务。我们提出了一个基于 LLMs 的框架，用于改进模型的性能和效率。实验结果表明 LLMs 在数据预处理中具有巨大潜力。