malware_rl
by bfilar
Malware Bypass Research using Reinforcement Learning
AI summary
Malware evasion research
Enabling research into malware bypass using reinforcement learning environments
- stars
- 102
- forks
- 20
- watching
- 8
Similar projects
Found by comparing what the projects do, not just their names.
Malware attacker tool
An open-source reinforcement learning framework to generate adversarial examples for malware classification models.
Learning platform
Provides Julia code to generate figures and examples for an introductory reinforcement learning book
Malware scanner
Real-time malware detection and alert system for AWS S3 files
Malware scanner
Analyzes malware behavior and maps it to a Malware Behavior Catalog for better understanding
RL Benchmark
A benchmark suite for unsupervised reinforcement learning agents, providing pre-trained models and scripts for testing and fine-tuning agent performance.
RL Tutorial
A tutorial project providing an introduction to reinforcement learning with Stable Baselines3
RL framework
A framework for implementing complex reinforcement learning algorithms with flexibility and ease of implementation
RL hack
Flappy Bird hack using Reinforcement Learning
Recommender system toolkit
A collection of real-world datasets and practical reinforcement learning baselines for recommendation systems.
Security toolkit
An open-source project that explores the intersection of machine learning and security to develop tools for detecting vulnerabilities in web applications.
Safe RL Benchmark
A unified benchmark for safe reinforcement learning algorithms and environments.
Malware dataset
Provides machine learning datasets for malware analysis
Robustness tester
A toolbox for researching and evaluating robustness against attacks on machine learning models
RL library
A high-throughput reinforcement learning library with optimized synchronous and asynchronous implementations of policy gradients.
RL benchmarks
A collection of standardized environments and datasets for training and benchmarking algorithms in offline reinforcement learning