Honeypot security technique can also stop attacks in natural language processing
Borrowing a technique commonly used in cybersecurity to defend against these universal trigger-based attacks, researchers at the Penn State College of Information Sciences and Technology have developed a machine learning framework that can proactively defend against the same types of attacks in natural language processing applications 99% of the time.
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Honeypot security technique can also stop attacks in natural language processing
Jessica Hallman
July 28, 2021
UNIVERSITY PARK, Pa. As online fake news detectors and spam filters become more sophisticated, so do attackers’ methods to trick them including attacks through the “universal trigger.” In this learning-based method, an attacker uses a phrase or set of words to fool an indefinite number of inputs. A successful attack could mean more fake news appearing in your social media feed or spam reaching your email inbox.