Spam Filtering Using Inexact String Matching in Explicit Feature Space with On-Line Linear Classifiers

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The security of machine learning (2010, methodology): “Sculley et al. (2006) use inexact string matching in feature selection to defeat obfuscations of words in spam emails.”
Security Evaluation of Pattern Classifiers under Attack (2014, background): “For instance, in [52] inexact string matching was proposed to counteract word obfuscation attacks in spam filtering.”
Motivating the Rules of the Game for Adversarial Example Research (2018, background): “Sculley, Wachman, and Brodley [118] also designed a classiﬁcation system robust to such attacks.”
TextBugger: Generating Adversarial Text Against Real-world Applications (2018, background): “To defend against these attacks, several mechanisms have been proposed to obtain robust classifiers [5, 34].”
Behavior of Machine Learning Algorithms in Adversarial Environments (2010, background): “Sculley et al. [2006] use inexact string matching in feature selection to defeat obfuscations of words in spam emails.”
Adversarial machine learning (2019, methodology): “longer recognized by the filter) is another popular technique for evading spam filters [38, 58].”