cage-challenge-4
The TTCP CAGE Challenges are a series of public challenges instigated to foster the development of autonomous cyber defensive agents. This CAGE Challenge 4 (CC4) returns to a defence industry enterprise environment, and introduces a Multi-Agent Reinforcement Learning (MARL) scenario.
AI summary
Network defender
Developing an AI system to protect a network against cyber threats in a simulated environment
- stars
- 42
- forks
- 10
- watching
- 4
Similar projects
Found by comparing what the projects do, not just their names.
Cyber Defense Platform
An AI research challenge and platform to test autonomous cyber defense capabilities using a simulated environment.
Cyber Challenge
A challenge to develop AI-based solutions for autonomous cyber operations in a controlled environment.
Learning Challenge
A reinforcement learning challenge where participants are invited to solve an open-ended problem using various approaches.
Research Environment
A cyber security research environment for training and development of security human and autonomous agents.
Challenge set
A collection of cryptographic and coding challenges designed to test problem-solving skills
Cyber threat testing framework
Provides a collection of plans to test defenses against real-world cyber threats.
Agent Trainer
An AI project that aims to develop an agent that can navigate and interact with a Minecraft environment while learning from its opponents' strategies
Challenge solver
Solutions to Advent of Code challenges in C#
Machine Learning Tutorials
A collection of examples and code snippets teaching machine learning concepts to security professionals through hands-on Python projects
CTF challenge
A cryptographic challenge with three stages, requiring participants to solve digital signature and decryption problems within a limited timeframe.
Robust network trainer
Trains neural networks to be provably robust against adversarial examples using abstract interpretation techniques.
Federated learning defense system
A backdoor defense system for federated learning, designed to protect against data poisoning attacks by isolating subspace training and aggregating models with robust consensus fusion.
Container attacker
A tool designed to test the security of container environments by simulating various attacks and vulnerabilities.
CI training ground
A deliberately vulnerable CI/CD environment designed to teach security best practices through interactive challenges
Challenge solutions
A collection of writeups on solving Capture The Flag challenges using Python.
