Abstract
This paper presents a content-based approach to spam detection based on low-level information. Instead of the traditional ’bag of words’ representation, we use a ’bag of character n-grams’ representation which avoids the sparse data problem that arises in n-grams on the word-level. Moreover, it is language-independent and does not require any lemmatizer or ’deep’ text preprocessing. Based on experiments on Ling-Spam corpus we evaluate the proposed representation in combination with support vector machines. Both binary and term-frequency representations achieve high precision rates while maintaining recall on equally high level, which is a crucial factor for anti-spam filters, a cost sensitive application.
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Kanaris, I., Kanaris, K., Stamatatos, E. (2006). Spam Detection Using Character N-Grams. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds) Advances in Artificial Intelligence. SETN 2006. Lecture Notes in Computer Science(), vol 3955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752912_12
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DOI: https://doi.org/10.1007/11752912_12
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