SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder

Al Moubayed, N.; Breckon, T.P.; Matthews, P.C.; McGough, A.S.

Abstract

In This paper we present a novel approach to spam filtering and demonstrate its applicability with respect to SMS messages. Our approach requires minimum features engineering and a small set of labelled data samples. Features are extracted using topic modelling based on latent Dirichlet allocation, and then a comprehensive data model is created using a Stacked Denoising Autoencoder (SDA). Topic modelling summarises the data providing ease of use and high interpretability by visualising the topics using word clouds. Given that the SMS messages can be regarded as either spam (unwanted) or ham (wanted), the SDA is able to model the messages and accurately discriminate between the two classes without the need for a pre-labelled training set. The results are compared against the state-of-the-art spam detection algorithms with our proposed approach achieving over 97 % accuracy which compares favourably to the best reported algorithms presented in the literature.

Citation

Al Moubayed, N., Breckon, T., Matthews, P., & McGough, A. (2016, August). SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder

• Presentation Conference Type: Conference Paper (published)
• Acceptance Date: Jun 16, 2016
• Online Publication Date: Aug 13, 2016
• Publication Date: Aug 13, 2016
• Deposit Date: Jun 17, 2016
• Publicly Available Date: Aug 13, 2017
• Print ISSN: 0302-9743
• Publisher: Springer Verlag
• Volume: 2
• Pages: 423-430
• Series Title: Lecture notes in computer science
• Series Number: 9886
• Series ISSN: 0302-9743,1611-3349
• Book Title: Artificial neural networks and machine learning – ICANN 2016 : 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016 ; proceedings. Part II.
• ISBN: 9783319447803
• DOI: https://doi.org/10.1007/978-3-319-44781-0_50
• Keywords: topic modelling, text processing, deep learning
• Public URL: https://durham-repository.worktribe.com/output/1150253
• Related Public URLs: https://arxiv.org/abs/1606.05554