Computer scientists' new tool fools hackers into sharing keys for better cybersecurity

The method, called DEEP-Dig (DEcEPtion DIGging), ushers intruders into a decoy site so the computer can learn from hackers' tactics. The information is then used to train the computer to recognize and stop future attacks.

computer science degree jobs Dallas researchers presented a paper on their work, "Improving Intrusion Detectors by Crook-Sourcing," at the annual Computer Security Applications Conference in December in Puerto Rico. They presented another paper, "Automating Cyberdeception Evaluation with Deep Learning," in January at the Hawaii International Conference of System Sciences.

DEEP-Dig advances a rapidly growing cybersecurity field known as deception technology, which involves setting traps for hackers. Researchers hope that the approach can be especially useful for defense organizations.

"There are criminals trying to attack our networks all the time, and normally we view that as a negative thing," said Dr. Kevin Hamlen, Eugene McDermott Professor of computer science. "Instead of blocking them, maybe what we could be doing is viewing these attackers as a source of free labor. They're providing us data about what malicious attacks look like. It's a free source of highly prized data."

The approach aims to solve a major challenge to using artificial intelligence for cybersecurity: a shortage of data needed to train computers to detect intruders. The lack of data is due to privacy concerns. Better data will mean better ability to detect attacks, said Gbadebo Ayoade MS'14, PhD'19, who presented the findings at the recent conferences.