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Algorithm May Be the Key to Timely, Inexpensive Cyber Defense


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A hacker.

A team led by researchers at The Pennsylvania State University used a machine learning approach based on reinforcement learning to create an adaptive cyber defense against zero-day attacks.

Credit: Pixahive

A team of researchers led by The Pennsylvania State University (Penn State) has developed an adaptive cyber defense against zero-day attacks using machine learning.

The new technique offers a powerful, cost-effective alternative to the moving target defense method used to detect and respond to cyberattacks.

Reinforcement learning enables the decision maker to learn to make the right choices by choosing actions that maximize rewards.

Said Penn State's Peng Liu, "The decision maker learns optimal policies or actions through continuous interactions with an underlying environment, which is partially unknown. So, reinforcement learning is particularly well-suited to defend against zero-day attacks when critical information—the targets of the attacks and the locations of the vulnerabilities—is not available."

From Penn State News
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA


 

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