SMS Spam Detection Through Skip-gram Embeddings and Shallow Networks

Abstract

The drastic decrease in mobile SMS costs turned phone users more prone to spam messages, usually with unwanted marketing or questionable content. As such, researchers have proposed different methods for detecting SMS spam messages. This paper presents a technique for embedding SMS messages into vector spaces that is suitable for spam detection. The proposed approach relies on mining patterns that are relevant for distinguishing spam from legitimate messages. A subset of those patterns is used to construct a function that maps text messages into a multidimensional vector space. The extracted patterns are represented as skip-grams of token attributes, where a skip-gram can be seen as a generalization of the n-gram model that allows a distance greater than one between matched tokens in the text. We evaluate the proposed approach using the generated vectors for spam classification on the UCI Spam Collection dataset. The experiments showed that our method combined with shallow networks reached accuracy that is competitive with state-of-the-art approaches.

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“…1 summarizes the environment and data in which the methodology was tested; performance was evaluated in terms of accuracy (ACC), F1-score (F1), precision (P), and recall (R). Downloaded datasets and existing methods in the literature [24,[27][28][29][30][31][32][33][34][35][36] were used for performance evaluation assuming an anonymous attack through malicious email. As a result of classification, liked and separated corpus were distinguished.…”