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
-grams’ representation which avoids the sparse data problem that arises in
-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.