Konsep Inti
Autonomous cyber defense agents can augment human defenders by automating critical steps in the cyber defense life cycle, but significant challenges must be overcome to enable their practical adoption.
Abstrak
The article discusses the path towards practical autonomous cyber defense agents, focusing on the use of reinforcement learning (RL) as a promising approach. It highlights several key challenges that need to be addressed:
Defining the right "game" for the autonomous agents to play: Cybersecurity cannot be reduced to a single game, and the environment in which the agent operates may change dynamically. Careful design of the observation space, reward function, and actions is crucial for the agent to be usable and effective in a real network.
Ensuring adaptability of the agents: Autonomous agents need to be adaptable to varying network environments, evolving adversary behaviors, and different organizational priorities in the CIA (confidentiality, integrity, availability) triangle. Current RL algorithms have limitations in this regard, and novel approaches are needed to address the challenge of adaptability.
Developing better training environments: High-fidelity simulation and emulation environments are required to train autonomous agents that can generalize well and be efficiently transferred to operational networks. Existing environments fall short in providing the necessary level of realism and flexibility.
The article suggests that a multi-agent approach, where each agent specializes in a specific stage or function within the cyber defense life cycle, is likely the best path forward to create reliable autonomous agents for cyber defense. This modular approach can make the creation, testing, deployment, and integration of these agents easier for security operations centers (SOCs) to adopt.
The authors also highlight the importance of standardized training environments that allow researchers to focus on developing the science of autonomous agents for cyber defense, rather than having to create their own experimental environments from scratch.
Statistik
"Defenders are overwhelmed by the number and scale of attacks against their networks."
"The creation of autonomous cyber defense agents is one promising approach to automate operations and prevent cyber defenders from being overwhelmed."
"Reinforcement learning (RL) addresses the challenge of 'learning from interaction with an environment in order to achieve long-term goals', where 'long-term goals' could include protecting a network against cyber attacks."
"Because reinforcement learning has demonstrated the ability to defeat human adversaries in complex games with large state spaces, it is a natural choice for creating defensive cyber agents."
Kutipan
"Could autonomous RL agents be used to help defenders delay and deny attackers?"
"Could autonomous RL agents be leveraged by defenders to automate pen testing?"
"Could autonomous RL agents be leveraged by attackers to overwhelm or sneak past defenders?"