Deep reinforcement learning offers smarter cybersecurity by detecting issues earlier , giving the opportunity to take preemptive steps to stop a cyberattack
Deep reinforcement learning offers smarter cybersecurity by detecting issues earlier , giving the opportunity to take preemptive steps to stop a cyberattack
WRITTEN BY : GEORGE HOPKIN
US researchers say deep reinforcement learning can offer a way for artificial intelligence to help protect computer networks in a world where state-sponsored hacking groups rub shoulders on the Dark Web with more traditional black hat types .
But the researchers say cybersecurity staff can relax , and even though businesses worldwide are reporting increases in ransomware , they ’ re not about to be replaced by an AI-powered workforce . For now .
Researchers with the Department of Energy ' s Pacific Northwest National Laboratory developed a simulation environment to test multistage attack scenarios involving different types of adversaries . This dynamic attack-defence simulation environment allowed them to compare the effectiveness of different AI-based defensive methods under controlled test settings .
According to their research , deep reinforcement learning ( DRL ) was effective at stopping adversaries from reaching their goals up to 95 % of the time in simulations of sophisticated cyberattacks .
While other forms of artificial intelligence are standard for detecting intrusions or filtering spam messages , deep reinforcement learning expands defenders ' abilities to orchestrate sequential decisionmaking plans in their daily face-off with adversaries . Deep reinforcement learning offers smarter cybersecurity by detecting changes in the cyber landscape earlier and the opportunity to take preemptive steps to scuttle a cyberattack .
“ An effective AI agent for cybersecurity needs to sense , perceive , act and adapt , based on the information it can gather
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