Researchers from the University of Cambridge have developed a novel approach to network defence using reinforcement learning, a breakthrough that could shift the balance of power in the ongoing cybersecurity arms race. The team, led by Myles Foley, Chris Hicks, Kate Highnam, and Vasilios Mavroudis, has demonstrated that autonomous agents can effectively defend against advanced persistent threats (APTs) in realistic network environments.
In their study, the researchers highlight the inherent disadvantage faced by network defenders, who must successfully counter every malicious attack, while attackers need only a single success to breach defences. To address this asymmetry, the team investigated the potential of reinforcement learning (RL) to train autonomous agents capable of defending against continual attacks.
The researchers designed a network environment simulation featuring 13 hosts across three subnets, mimicking a realistic network infrastructure. Within this environment, they trained a reinforcement learning agent to defend against two distinct APT red agents. The first APT agent possessed complete knowledge of the network layout, while the second had to discover resources through exploration, making it more general but potentially slower.
The trained RL agent demonstrated impressive capabilities, reliably defending against both types of APT agents. The agent’s success underscores the potential of reinforcement learning to create adaptive, autonomous defenders that can keep pace with evolving cyber threats. By learning from each encounter, the RL agent continuously improves its defence strategies, making it a formidable opponent for even the most sophisticated attackers.
The practical applications of this research are significant. Autonomous network defence agents could revolutionise cybersecurity by providing continuous, adaptive protection against a wide range of threats. These agents could be deployed in various network environments, from corporate infrastructures to critical national systems, enhancing overall security and reducing the burden on human defenders.
Moreover, the use of reinforcement learning in network defence aligns with broader trends in artificial intelligence and machine learning, where autonomous systems are increasingly being tasked with complex decision-making roles. As cyber threats continue to evolve, the development of autonomous defence mechanisms will be crucial in maintaining the upper hand in the network security arms race.
The research conducted by the University of Cambridge team represents a significant step forward in the field of autonomous network defence. By leveraging the power of reinforcement learning, they have demonstrated a viable path to creating adaptive, intelligent defenders capable of countering the ever-growing sophistication of cyber threats. As this technology matures, it has the potential to reshape the landscape of network security, providing a robust and scalable solution to the challenges faced by defenders worldwide. Read the original research paper here.