Binary LNS-based Naïve Bayes Hardware Classifier for Spam Control

We propose a hardware architecture for a naive Bayes classifier in the context of e-mail classification for spam control. Our proposal presents a word-serial naive Bayes classifier architecture that utilizes the logarithmic number system (LNS) to reduce the computational complexity. We present the hardware architecture for non-iterative binary LNS recoding using a look-up table approach. Our design was synthesized targeting an Altera Stratix CPLD device. The synthesized classifier was functionally verified with a MATLAB implementation. Our binary LNS naive Bayes classifier exhibits high e-mail classification throughput of more than 30 thousands e-mails per second.