Sms spam filtering using probabilistic topic modelling and stacked denoising autoencoder

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9887 LNCS, Issue , 2016, Pages 423-430

  Al Moubayed, Noura ^a   Breckon, Toby ^a   Matthews, Peter ^a   McGough, A Stephen ^a  

^a DURHAM UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

MACHINE LEARNING; STATISTICS;

AUTO ENCODERS; INTERPRETABILITY; LATENT DIRICHLET ALLOCATION; SMS MESSAGES; SPAM DETECTION; SPAM FILTERING; STATE OF THE ART; TRAINING SETS;

NEURAL NETWORKS;

EID: 84988353671     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-44781-0_50     Document Type: Conference Paper

References (21)

1. Almeida, T.A., Hidalgo, J.M.G., Yamakami, A.: Contributions to the study of sms spam filtering: new collection and results. In: Proceedings of the 11th ACM Symposium on Document Engineering, pp. 259–262. ACM (2011)
2. Almeida, T.A., Yamakami, A.: Facing the spammers: a very effective approach to avoid junk e-mails. Expert Syst. Appl. 39(7), 6557–6561 (2012)
3. Bengio, Y., Courville, A.C., Vincent, P.: Unsupervised feature learning and deep learning: a review and new perspectives, vol. 1 (2012). CoRR, abs/1206.5538
4. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. mach. Learn. Res. 3, 993–1022 (2003)
5. Delany, S.J., Buckley, M., Greene, D.: Sms spam filtering: methods and data. Expert Syst. Appl. 39(10), 9899–9908 (2012)
6. Gómez Hidalgo, J.M., Bringas, G.C., Sánz, E.P., García, F.C.: Content based sms spam filtering. In: Proceedings of the 2006 ACM Symposium on Document Engineering, pp. 107–114. ACM (2006)
7. Groupe Speciale Mobile Association (GSMA): SMS spams and mobile messaging attacks-introduction, trends and examples (2011)
8. Gupta, M., Gao, J., Aggarwal, C., Han, J.: Outlier detection for temporal data. Synth. Lect. Data Min. Knowl. Discov. 5(1), 1–129 (2014)
9. Guzella, T.S., Caminhas, W.M.: A review of machine learning approaches to spam filtering. Expert Syst. Appl. 36(7), 10206–10222 (2009)
10. Hawkins, S., He, H., Williams, G.J., Baxter, R.A.: Outlier detection using replicator neural networks. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2002. LNCS, vol. 2454, pp. 170–180. Springer, Heidelberg (2002)
11. Healy, M., Delany, S.J., Zamolotskikh, A.: An assessment of case base reasoning for short text message classification. In: Conference papers, p. 42 (2004)
12. Jie, H., Bei, H., Wenjing, P.: A bayesian approach for text filter on 3g network. In: 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), pp. 1–5. IEEE (2010)
13. Johnson, N.L., Kotz, S., Balakrishnan, N.: Continuous Multivariate Distributions, volume 1, Models and Applications, vol. 59. Wiley, New York (2002)
14. PortioResearch: Mobile Messaging Futures 2013–2017 (2013)
15. Scholkopft, B., Mullert, K.R.: Fisher discriminant analysis with kernels. Neural Netw. sig. proc. IX 1, 1 (1999)
16. Smith, A.: The Smartphone Difference. Pew Research Center, Washington (2015)
17. Sohn, D.N., Lee, J.T., Rim, H.C.: The contribution of stylistic information to content-based mobile spam filtering. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pp. 321–324. Association for Computational Linguistics (2009)
18. Steyvers, M., Griffiths, T.: Latent Semantic Analysis: A Road to Meaning, Chapter Probabilistic Topic Models. Laurence Erlbaum, Hillsdale (2007)
19. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)
20. Xiang, Y., Chowdhury, M., Ali, S.: Filtering mobile spam by support vector machine. In: CSITeA 2004: Third International Conference on Computer Sciences, Software Engineering, Information Technology, E-Business and Applications, pp. 1–4. International Society for Computers and Their Applications (ISCA) (2004)
21. Zimek, A., Schubert, E., Kriegel, H.P.: A survey on unsupervised outlier detection in high-dimensional numerical data. Stat. Anal. Data Min. 5(5), 363–387 (2012)