A Linguistics-Based Attack on Personalised Statistical E-mail Classifiers
Abstract:
We present a potential vulnerability of personalised anti-spam filters where an attacker sends carefully constructed e-mail messages with the goal of negatively affecting classifier accuracy. Words from the core of the English language are randomly "injected" into spam e-mails for the express purpose of manipulating the probability tables of a naive Bayesian classifier. This attack method is shown to be successful in reducing classifier accuracy within a laboratory environment. Barriers to a real-world implementation and potential countermeasures are discussed.
Tech Report Number:
CS-2004-06
Report Date:
March 25, 2004
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