Artículos de revistas
Occam's Razor-based Spam Filter
Registro en:
Journal Of Internet Services And Applications. , v. 3, n. 3, p. 245 - 253, 2012.
18674828
10.1007/s13174-012-0067-x
2-s2.0-84888619395
Autor
Almeida T.A.
Yamakami A.
Institución
Resumen
Nowadays e-mail spam is not a novelty, but it is still an important rising problem with a big economic impact in society. Spammers manage to circumvent current spam filters and harm the communication system by consuming several resources, damaging the reliability of e-mail as a communication instrument and tricking recipients to react to spam messages. Consequently, spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. In this paper, we present a novel approach to spam filtering based on theminimum description length principle. Furthermore, we have conducted an empirical experiment on six public and real non-encoded datasets. The results indicate that the proposed filter is fast to construct, incrementally updateable and clearly outperforms the state-of-the-art spam filters. © The Brazilian Computer Society 2012. 3 3 245 253 Almeida, T., Yamakami, A., Content-based spam filtering (2010) Proceedings of the 23rd IEEE International Joint Conference On Neural Networks, pp. 1-7. , Barcelona, Spain Almeida, T., Yamakami, A., Redução de Dimensionalidade Aplicada na Classificação de Spams Usando Filtros Bayesianos (2011) Revista Brasileira De Computação Aplicada, 3 (1), pp. 16-29 Almeida, T., Yamakami, A., Almeida, J., Evaluation of approaches for dimensionality reduction applied with Naive Bayes anti-spam filters (2009) Proceedings of the 8th IEEE International Conference On Machine Learning and Applications, pp. 517-522. , Miami, FL, USA Almeida, T., Yamakami, A., Almeida, J., Filtering spams using the minimum description length principle (2010) Proceedings of the 25th ACM Symposium On Applied Computing, pp. 1856-1860. , Sierre, Switzerland Almeida, T., Yamakami, A., Almeida, J., Probabilistic antispam filtering with dimensionality reduction (2010) Proceedings of the 25th ACM Symposium On Applied Computing, pp. 1804-1808. , Sierre, Switzerland Almeida, T., Hidalgo, J.G., Yamakami, A., Contributions to the study of SMS spam filtering: New collection and results (2011) Proceedings of the 2011 ACM Symposium On Document Engineering, pp. 259-262. , Mountain View, CA, USA Almeida, T., Almeida, J., Yamakami, A., Spam filtering: How the dimensionality reduction affects the accuracy of Naive Bayes classifiers (2011) J Internet Serv Appl, 1 (3), pp. 183-200 Almeida, T.A., Yamakami, A., Advances in spam filtering techniques (2012) Com Putational Intelligence For Privacy and Security. Studies In Computational Intelligence, 394, pp. 199-214. , In: Elizondo D, Solanas A,Martinez-Balleste A (eds), Springer, Berlin Almeida, T.A., Yamakami, A., Facing the spammers: A very effective approach to avoid junk e-mails (2012) Expert Syst Appl, pp. 1-5 Anagnostopoulos, A., Broder, A., Punera, K., Effective and efficient classification on a search-engine model (2008) Knowl Inf Syst, 16 (2), pp. 129-154 Androutsopoulos, I., Koutsias, J., Chandrinos, K., Paliouras, G., Spyropoulos C (2000a) An evalutation of Naive Bayesian anti-spam filtering Proceedings of the 11th European Conference On Machine Learning, pp. 9-17. , Barcelona, Spain Androutsopoulos, I., Paliouras, G., Karkaletsis, V., Sakkis, G., Spyropoulos, C., Stamatopoulos, P., Learning to filter spam e-mail: A comparison of a Naive Bayesian and a memory-based approach (2000) Proceedings of the 4th European Conference On Principles and Practice of Knowledge Discovery In Databases, pp. 1-13. , Lyon, France Androutsopoulos, I., Paliouras, G., Michelakis, E., (2004) Learning to Filter Unsolicited Commercial E-mail, , Technical Report 2004/2, National Centre for Scientific Research "Demokritos", Athens, Greece Baldi, P., Brunak, S., Chauvin, Y., Andersen, C., Nielsen, H., Assessing the accuracy of prediction algorithms for classification: An overview (2000) Bioinformatics, 16 (5), pp. 412-424 Barron, A., Rissanen, J., Yu, B., The minimum description length principle in coding and modeling (1998) IEEE Trans Inf Theory, 44 (6), pp. 2743-2760 Blanzieri, E., Bryl, A., A survey of learning-based techniques of email spam filtering (2008) Artif Intell Rev, 29 (1), pp. 335-455 Bordes, A., Ertekin, S., Weston, J., Bottou, L., Fast kernel classifiers with online and active learning (2005) J Mach Learn Res, 6, pp. 1579-1619 Bratko, A., Cormack, G., Filipic, B., Lynam, T., Zupan, B., Spam filtering using statistical data compression models (2006) J Mach Learn Res, 7, pp. 2673-2698 Carreras, X., Marquez, L., Boosting trees for anti-spam email filtering (2001) Proceedings of the 4th International Conference On Recent Advances In Natural Language Processing, pp. 58-64. , Tzigov Chark, Bulgaria Cohen, W., Fast effective rule induction (1995) Proceedings of 12th International Conference On Machine Learning, pp. 115-123. , Tahoe City, CA, USA Cohen, W., Learning rules that classify e-mail (1996) Proceedings of the AAAI Spring Symposium On Machine Learning In Information Access, pp. 18-25. , CA, USA, Stanford Cormack, G., Email spam filtering: A systematic review (2008) Found Trends Inf Retr, 1 (4), pp. 335-455 Cormack, G., Lynam, T., Online supervised spam filter evaluation (2007) ACM Trans Inf Syst, 25 (3), pp. 1-11 Czarnowski, I., Cluster-based instance selection for machine classification (2011) Knowl Inf Syst Drucker, H., Wu, D., Vapnik, V., Support vector machines for spam categorization (1999) IEEE Trans Neural Netw, 10 (5), pp. 1048-1054 Forman, G., Scholz, M., Rajaram, S., Feature shaping for linear SVM classifiers (2009) Proceedings of the 15th ACM SIGKDD International Conference On Knowledge Discovery and Data Mining, pp. 299-308. , France, Paris Frank, E., Chui, C., Witten, I., Text categorization using compression models (2000) Proceedings of the 10th Data Compression Conference, pp. 555-565. , Snowbird, UT, USA Grünwald, P., Atutorial introduction to theminimum description length principle (2005) Advances In Minimum Description Length: Theory and Applications, pp. 3-81. , In: Grünwald P, Myung I, Pitt M (eds), MIT Press, Cambridge Guzella, T., Caminhas, W., A review of machine learning approaches to spam filtering (2009) Expert Syst Appl, 36 (7), pp. 10206-10222 Hidalgo, J., Evaluating cost-sensitive unsolicited bulk mail categorization (2002) Proceedings of the 17th ACM Symposium On Applied Computing, pp. 615-620. , Madrid, Spain Joachims, T., A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization (1997) Proceedings of 14th International Conference On Machine Learning, pp. 143-151. , Nashville, TN, USA John, G., Langley, P., Estimating continuous distributions in Bayesian classifiers (1995) Proceedings of the 11th International Conference OnUncertainty In Artificial Intelligence, pp. 338-345. , Montreal,Canada Katakis, I., Tsoumakas, G., Vlahavas, I., Tracking recurring contexts using ensemble classifiers: An application to email filtering (2009) Knowl Inf Syst, 22 (3), pp. 371-391 Kolcz, A., Alspector, J., SVM-based filtering of e-mail spam with content-specific misclassification costs (2001) Proceedings of the 1st International Conference On Data Mining, pp. 1-14. , San Jose, CA, USA Losada, D., Azzopardi, L., Assessing multivariate Bernoulli models for information retrieval (2008) ACM Trans Inf Syst, 26 (3), pp. 1-46 Matthews, B., Comparison of the predicted and observed secondary structure of T4 phage lysozyme (1975) Biochimica Et Biophysica Acta, 405 (2), pp. 442-451 McCallum, A., Nigam, K., A comparison of event models for Naive Bayes text classication (1998) Proceedings of the 15th AAAI Workshop On Learning For Text Categorization, pp. 41-48. , Menlo Park, CA, USA Metsis, V., Androutsopoulos, I., Paliouras, G., Spam filtering with Naive Bayes-which Naive Bayes? (2006) Proceedings of the 3rd International Conference On Email and Anti-Spam, pp. 1-5. , Mountain View, CA, USA Peng, T., Zuo, W., He, F., SVM based adaptive learning method for text classification from positive and unlabeled documents (2008) Knowl Inf Syst, 16 (3), pp. 281-301 Reddy, C., Park, J.-H., Multi-resolution boosting for classification and regression problems (2010) Knowl Inf Syst Rissanen, J., Modeling by shortest data description (1978) Automatica, 14, pp. 465-471 Sahami, M., Dumais, S., Hecherman, D., Horvitz, E., A Bayesian approach to filtering junk e-mail (1998) Proceedings of the 15th NationalConference On Artificial Intelligence, pp. 55-62. , Madison, WI,USA Schapire, R., Singer, Y., Singhal, A., Boosting and Rocchio applied to text filtering (1998) Proceedings of the 21st Annual International Conference On Information Retrieval, pp. 215-223. , Melbourne, Australia Schneider, K., On word frequency information and negative evidence in Naive Bayes text classification (2004) Proceedings of the 4th International Conference On Advances In Natural Language Processing, pp. 474-485. , Alicante, Spain Siefkes, C., Assis, F., Chhabra, S., Yerazunis, W., Combining winnow and orthogonal sparse bigrams for incremental spam filtering (2004) Proceedings of the 8th European Conference On Principles and Practice of Knowledge Discovery In Databases, pp. 410-421. , Pisa, Italy Song, Y., Kolcz, A., Gilez, C., Better Naive Bayes classification for high-precision spam detection (2009) Softw Pract Experience, 39 (11), pp. 1003-1024 Teahan, W., Harper, D., Using compression-based language models for text categorization (2001) Proceedings of the 2001 Workshop On Language Modeling and Information Retrieval, pp. 1-5. , Pittsburgh, PA, USA Wozniak, M., A hybrid decision tree training method using data streams (2010) Knowl Inf Syst Wu, X., Kumar, V., Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G., Steinberg, D., Top 10 algorithms in data mining (2008) Knowl Inf Syst, 14 (1), pp. 1-37 Zhang, J., Kang, D., Silvescu, A., Honavar, V., Learning accurate and concise Naive Bayes classifiers from attribute value taxonomies and data (2006) Knowl Inf Syst, 9 (2), pp. 157-179 Zhang, L., Zhu, J., Yao, T., An evaluation of statistical spam filtering techniques (2004) ACMTrans Asian Lang Inf Process, 3 (4), pp. 243-269