• Researchers are developing a new cybersecurity method that draws inspiration from the human body.
  • Using machine learning, the system would be taught to recognize various cyber threats and trigger appropriate responses.

The big picture

Researchers at the University of Arizona are working on a new cybersecurity method that can detect threats in the early stages.

  • Inspired by the human body, the cybersecurity method would respond to dangerous threats similar to the immune system that pulls the hand away on touching a hot object.
  • The researchers plan on using machine-learning techniques to teach computers to recognize cyber threats faster.

“Once you put your hand down on that stove, you know not to touch it again, because it’s hot. But how can you be prepared to recognize other dangers, like putting your hand in a toaster? This is where machine learning comes in,” said Gregory Ditzler, co-investigator and assistant professor of electrical and computer engineering.

Going into the details

Machine learning is a form of artificial intelligence that makes the computers capable of learning and recognizing patterns.

  • The research team will expose the computer to a number of cybersecurity threats to enable the computer to recognize them.
  • Creating a playbook of defense measures is also on the researchers’ agenda. This will help the system respond appropriately to various threats.
  • Traditional cyberdefense measures involve reaction strategies after the damage has been done.
  • With bad actors constantly polishing their methods, this is a step to train computers to predict an attack before it happens by detecting changes in the environment.

“An attacker can reach hundreds of thousands of devices in a fraction of a second, so we need our ability to detect threats and protect a system to work just as quickly,’’ said Salim Hariri, UA electrical and computer engineering professor and the principal investigator of the project.

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