Tiago A. Almeida and Akebo Yamakami Compression-based spam filter Security and Communication Networks
E-mail spam is still an important problem with a high impact on the economy. Spam filtering poses a special problem in text categorization, in which the defining characteristic is that filters face an active adversary. This paper presents a novel approach to spam filtering based on a compression-based model. Experiments were conducted on public and real non-encoded datasets. The results indicate that the proposed filter is fast to construct, incrementally updateable, and outperforms established spam classifiers.
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