Scientists have taken a key step toward harnessing a form of AI known as deep reinforcement learning, or DRL, to protect computer networks.
and presented their work Feb. 14 at a workshop on AI for Cybersecurity during the annual meeting of the Association for the Advancement of Artificial Intelligence in Washington, D.C.
are standard to detect intrusions or filter spam messages, deep reinforcement learning expands defenders’ abilities to orchestrate sequential decision-making plans in their daily face-off with adversaries., the ability to detect changes in the cyber landscape earlier, and the opportunity to take preemptive steps to scuttle a cyberattack.
“It’s the same concept in reinforcement learning,” Chatterjee said. “The agent can choose from a set of actions. With each action comes feedback, good or bad, that becomes part of its memory. There’s an interplay between exploring new opportunities and exploiting past experiences. The goal is to create an agent that learns to make good decisions.
“Our algorithms operate in a competitive environment — a contest with an adversary intent on breaching the system,” said Chatterjee. “It’s a multistage attack, where the adversary can pursue multiple attack paths that can change over time as they try to go from reconnaissance to exploitation. Our challenge is to show how defenses based on deep reinforcement learning can stop such an attack.
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