“One very promising part of this work is that our general framework can defend against different types of attacks,” said Ren Wang, a research fellow in electrical and computer engineering, who was primarily responsible for the development and implementation of the software.

The researchers used image identification as the test case, evaluating RAILS against eight types of adversarial attacks in several datasets. It showed improvement in all cases, including protection against the most damaging type of adversarial attack—known as a Projected Gradient Descent attack. In addition, RAILS improved the overall accuracy. For instance, it helped correctly identify an image of a chicken and an ostrich, widely perceived as a cat and a horse, as two birds.

“This is an amazing example of using mathematics to understand this beautiful dynamical system,” Rajapakse said. “We may be able to take what we learned from RAILS and help reprogram the immune system to work more quickly.”

Future efforts from Hero’s team will focus on reducing the response time from milliseconds to microseconds.

Hero is also the R. Jamison and Betty Williams Professor of Engineering and a professor of electrical engineering and computer science, biomedical engineering and statistics. Rajapakse is also an associate professor of mathematics and of biomedical engineering. Lindsly is now at MathWorks.

The project was funded by the Department of Defense, Defense Advanced Research Projects Agency and the Army Research Office.

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