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This paper provides an overview of current and potential future spam filtering techniques. We examine the problems spam introduces, what spam is and how we can measure it. The paper primarily focuses on automated, noninteractive filters, with a broad review ranging from commercial implementations to ideas confined to current research papers. Both machine learning and non-machine learning based filters are reviewed as potential solutions and a taxonomy of known approaches presented. While a range of different techniques have and continue to be evaluated in academic research, heuristic and Bayesian filtering -along with its variantsprovide the greatest potential for future spam prevention.

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