BenDerPan

We use LLMs to extract relevant knowledge entities from cybersecurity-related texts. We use a subset of arXiv preprints on cybersecurity as our data and compare different LLMs in terms of entity recognition (ER) and relevance. The results suggest that LLMs do not produce good knowledge entities that reflect the cybersecurity context.

000

apache-log4j-poc

Apache Log4j 远程代码执行

Language:Java010

ChainKnowledgeGraph

ChainKnowledgeGraph, 产业链知识图谱包括A股上市公司、行业和产品共3类实体，包括上市公司所属行业关系、行业上级关系、产品上游原材料关系、产品下游产品关系、公司主营产品、产品小类共6大类。上市公司4,654家，行业511个，产品95,559条、上游材料56,824条，上级行业480条，下游产品390条，产品小类52,937条，所属行业3,946条。

Language:Python000

csharpfbp

C# Implementation of Flow-Based Programming (FBP)

Language:C#Artistic-2.0000

CyberBattleSim

An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.

MIT000

dot

The Deepfake Offensive Toolkit

Language:PythonBSD-3-Clause000

DotNetMonitor

Think APIMonitor, but for .NET binaries.

Language:C#000

dyalect

Dyalect is a dynamic programming language for .NET Core.

Language:C#MIT010

GymGodot

Reinforcement learning with Godot

Language:GDScriptMIT000

InScan

边界打点后的自动化渗透工具

Language:Go000

instructGOOSE

Implementation of Reinforcement Learning from Human Feedback (RLHF)

Language:Jupyter NotebookMIT000

MARL-Algorithms

Implementations of IQL, QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II

Language:Python000