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This paper provides an overview of current and potential future spam filtering approaches. We examine the problems spam introduces, what spam is and how we can measure it. The paper primarily focuses on automated, non-interactive 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 is presented. While a range of different techniques have and continue to be evaluated in academic research, heuristic and Bayesian filtering dominate commercial filtering systems; therefore, a case study of these techniques is presented to demonstrate and evaluate the effectiveness of these popular techniques. |
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