Yawning Titan
An open-source, graph-based cyber-security simulation environment for training intelligent agents in autonomous cyber defense operations.
Yawning Titan
An open-source, graph-based cyber-security simulation environment for training intelligent agents in autonomous cyber defense operations.
Yawning Titan
SKU: yawning-titan
Yawning Titan is an abstract, graph-based cyber-security simulation environment designed to facilitate the training of intelligent agents for autonomous cyber operations. Developed by the Defence Science and Technology Laboratory (Dstl), it focuses on enabling defensive autonomous agents to counter probabilistic red (attacker) agents within simulated network environments. Built on OpenAI's Gym framework, Yawning Titan supports a wide range of reinforcement learning algorithms and offers flexible environment configurations, making it a valuable tool for research and development in cyber defense strategies.
Training reinforcement learning agents for autonomous cyber defense.
Simulating cyber-security scenarios to test defensive strategies.
Developing and evaluating AI-driven responses to network intrusions.
Researching agent generalization across varying network topologies.
Yawning Titan is designed for high autonomy in simulated cyber defense scenarios, enabling blue agents to independently execute actions like node isolation and restoration using reinforcement learning (RL) policies. Its architecture supports real-time decision-making against probabilistic red agents without human intervention during episodes. However, autonomy is constrained to predefined simulation rules and network topologies, requiring human oversight for environment configuration, reward function design, and policy evaluation. The system demonstrates advanced autonomous capabilities within its operational domain but remains dependent on human-defined parameters and training frameworks.
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