"Cybersecurity Defenders Are Expanding Their AI Toolbox"
Deep Reinforcement Learning (DRL) is a form of Artificial Intelligence (AI) that scientists have taken a significant step toward using to defend computer networks. DRL was effective in preventing adversaries from achieving their goals up to 95 percent of the time when confronted with sophisticated cyberattacks in a rigorous simulation environment. The result suggests a potential role for autonomous AI in proactive cyber defense. Researchers at the Department of Energy's (DOE) Pacific Northwest National Laboratory (PNNL) documented their findings in a research paper and presented them at a workshop on AI for Cybersecurity during the annual meeting of the Association for the Advancement of AI in Washington, DC. The development of a simulation platform for testing multistage attack scenarios involving different types of adversaries was the initial step. The creation of such a dynamic attack-defense simulation environment allows researchers to examine the effectiveness of various AI-based defense strategies in controlled test conditions. Such tools are necessary for assessing the performance of DRL algorithms. The method is becoming an effective decision-support tool for cybersecurity experts. It provides a defense agent that can learn, quickly adapt, and make decisions autonomously. Although other forms of AI are commonly used to detect intrusions or filter spam messages, DRL enhances defenders' ability to orchestrate sequential decision-making plans in their everyday confrontations with attackers. According to the researchers, DRL offers smarter cybersecurity, the ability to detect changes in the cyber landscape earlier, and the chance to take preventative measures against a cyberattack. This article continues to discuss the PNNL scientist's research on DRL for cyber system defense under dynamic adversarial uncertainties.