When hackers attempt to break into a secure system, not many people would welcome them in, but that is the approach developed by computer scientists at the University of Texas at Dallas.
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.
UT 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,” says Kevin Hamlen, PhD, 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.”
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