Redes Neurais Artificiais para Detecção de Web Spams
Resumo
Web spam tem se tornado um problema frequente na vida dos usuários da Internet, provocando prejuízos pessoais e economicos. Vários métodos vem sendo propostos para detecção de web spam, porém a alta velocidade de aperfeiçoamento das técnicas usadas pelos spammers exige que os métodos de classificação sejam cada vez mais genéricos e eficientes. Diante disso, esse trabalho apresenta uma analise de desempenho de redes neurais perceptron de multiplas camadas empregadas para combater esse problema.
Palavras-chave:
Redes Neurais Artificiais, Detecção, Web Spams
Referências
Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Clarendon Press.
Castillo, C., Donato, D., & Gionis, A. (2007). Know your neighbors: web spam detection using the web topology. In Proc. of the SIGIR, 423–430, Amsterdam, The Netherlands.
Egele, M., Kolbitsch, C., & Platzer, C. (2011). Removing web spam links from search engine results. Journal in Computer Virology, 7:51–62.
Erdelyi, M., Garzó, A., & Benczúr, A. A. (2011). Web spam classification: a few features worth more. In Proc. of the 2011 WebQuality, 27–34, Hyderabad, India.
Gan, Q. & Suel, T. (2007). Improving web spam classifiers using link structure. In Proc. of the 3rd AIRWeb, 17–20, Banff, Alberta, Canada.
Geng, G., Wang, C., Li, Q., Xu, L., & Jin, X. (2007). Boosting the performance of web spam detection with ensemble under-sampling classification. In Proc. of the 14th FSKD, 583–587, Haikou, China.
Gyongyi, Z. & Garcia-Molina, H. (2005). Spam: it’s not just for inboxes anymore. Computer, 38(10):28–34.
Hagan, M. T. & Menhaj, M. B. (1994). Training feedforward networks with the marquardt algorithm. Neural Networks, IEEE Trans. on, 5(6):989–993.
Haykin, S. (2001). Redes Neurais - Príncipios e Praticas . Bookman, Sao Paulo.
Largillier, T. & Peyronnet, S. (2010). Lightweight clustering methods for webspam de motion. In Proc. of the IEEE/WIC/ACM IAT, 98–104, Toronto, Canada.
Qiu, B., Prinet, V., Perrier, E., & Monga, G. (2003). Multi-block pca method for image change detection. In Proc. of the 12th IAP, 385–390.
Shen, G., Gao, B., Liu, T., Feng, G., Song, S., & Li, H. (2006). Detecting link spam using temporal information. In Proc. of the 6th ICDM, 1049–1053.
Svore, K. M., Wu, Q., & Burges, C. J. (2007). Improving web spam classification using rank-time features. In Proc. of the 3rd AIRWeb, 9–16, Banff, Alberta, Canada.
Castillo, C., Donato, D., & Gionis, A. (2007). Know your neighbors: web spam detection using the web topology. In Proc. of the SIGIR, 423–430, Amsterdam, The Netherlands.
Egele, M., Kolbitsch, C., & Platzer, C. (2011). Removing web spam links from search engine results. Journal in Computer Virology, 7:51–62.
Erdelyi, M., Garzó, A., & Benczúr, A. A. (2011). Web spam classification: a few features worth more. In Proc. of the 2011 WebQuality, 27–34, Hyderabad, India.
Gan, Q. & Suel, T. (2007). Improving web spam classifiers using link structure. In Proc. of the 3rd AIRWeb, 17–20, Banff, Alberta, Canada.
Geng, G., Wang, C., Li, Q., Xu, L., & Jin, X. (2007). Boosting the performance of web spam detection with ensemble under-sampling classification. In Proc. of the 14th FSKD, 583–587, Haikou, China.
Gyongyi, Z. & Garcia-Molina, H. (2005). Spam: it’s not just for inboxes anymore. Computer, 38(10):28–34.
Hagan, M. T. & Menhaj, M. B. (1994). Training feedforward networks with the marquardt algorithm. Neural Networks, IEEE Trans. on, 5(6):989–993.
Haykin, S. (2001). Redes Neurais - Príncipios e Praticas . Bookman, Sao Paulo.
Largillier, T. & Peyronnet, S. (2010). Lightweight clustering methods for webspam de motion. In Proc. of the IEEE/WIC/ACM IAT, 98–104, Toronto, Canada.
Qiu, B., Prinet, V., Perrier, E., & Monga, G. (2003). Multi-block pca method for image change detection. In Proc. of the 12th IAP, 385–390.
Shen, G., Gao, B., Liu, T., Feng, G., Song, S., & Li, H. (2006). Detecting link spam using temporal information. In Proc. of the 6th ICDM, 1049–1053.
Svore, K. M., Wu, Q., & Burges, C. J. (2007). Improving web spam classification using rank-time features. In Proc. of the 3rd AIRWeb, 9–16, Banff, Alberta, Canada.
Publicado
16/05/2012
Como Citar
SILVA, Renato M.; ALMEIDA, Tiago A.; YAMAKAMI, Akebo.
Redes Neurais Artificiais para Detecção de Web Spams. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 8. , 2012, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2012
.
p. 749-754.
DOI: https://doi.org/10.5753/sbsi.2012.14459.
