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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. Image: Adobe Stock 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.

Dongwon leeInternational joint conference on natural language processingPenn state college of information sciencesYonsei universityMeeting of the association for computational linguisticsNational science foundationPenn state collegeInformation sciencesNoseong parkJoint conferenceAnnual meetingComputational linguisticsInternational joint conferenceNatural language processingNational scienceசர்வதேச கூட்டு மாநாடு ஆன் இயற்கை மொழி ப்ரோஸெஸிஂக்