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Python implementation of Evolutionary Game Theory paper Young (1993), utilizing Markov chains and graph theory, as well as, Edmond's algorithm and Sörensen-Janssens's algorithm.
A Python implementation for analyzing the emergence and stability of social hierarchies through evolutionary game theory and stochastic processes. Models strategic interactions between players usin…
HoneyIoT: An Intelligent-Interaction Honeypot for IoT Devices Through Reinforcement Learning
An adaptive AI honeypot system with attacker behavioral profiling and reinforcement learning capabilities
A collection of awesome software, libraries, documents, books, resources and cool stuff about industrial control systems in cybersecurity.
an awesome list of honeypot resources
ISF(Industrial Control System Exploitation Framework),a exploitation framework based on Python
A secure low code honeypot framework, leveraging LLM for System Virtualization.
RDP monster-in-the-middle (mitm) and library for Python with the ability to watch connections live or after the fact
source code of the paper "Anti-jamming Channel Allocation in UAV-enabled Edge Computing: A Stackelberg Game Approach" published in MSN 2022
use MATLAB to calculate and simulate the game, including Stackelberg game, Evolutionary game.
This is the numerical analysis of a game theory framework based on a carbon credit system within a public-private partnership is developed, involving Stackelberg and Collaborative games.
unofficial implementation of paper "Strategic Gradient Transmission with Targeted Privacy-Awareness in Model Training: A Stackelberg Game Analysis"
Create simple drawings of neural networks using graphviz
A Red Team Script to Detect Canary Tokens and Seed Files
A parallel framework for population-based multi-agent reinforcement learning.
Six Degrees of Domain Admin
A realistic simulator of Active Directory domains
Miscellaneous tools for BloodHound
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
A Python grid based implementation of Multi-Agent Reinforcement Learning with Prospect Theory