Um Método para Detecção de Bots Sociais Baseado em Redes Neurais Convolucionais Aplicadas em Mensagens Textuais
Resumo
Atualmente, as redes sociais estão sujeitas a ações de bots sociais que executam atividades maliciosas como a disseminação de notícias falsas. Algumas pesquisas voltadas à detecção desse tipo de malware se baseiam em estatísticas extraídas a partir do conteúdo das mensagens postadas. Como a extração de estatísticas pode ocasionar perda de informação, este trabalho tem como objetivo apresentar evidências experimentais de que o uso de textos originais das mensagens pode melhorar a precisão de detecção. Para tanto, propõe-se um método que aplica uma rede neural convolucional para identificar mensagens suspeitas. Resultados preliminares utilizando dados do Twitter se mostraram promissores, fornecendo indícios de adequação do método proposto.Referências
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Beaudry, N. J. and Renner, R. (2012). An intuitive proof of the data processing inequality. Quantum Info. Comput., 12(5-6):432–441.
Bezerra, E. (2016). Introdução à aprendizagem profunda. http://sbbd2016.fpc.ufba.br/sbbd2016/minicursos/minicurso3.pdf.
Chollet, F. et al. (2015). Keras. https://github.com/fchollet/keras.
Davis, C. A., Varol, O., Ferrara, E., Flammini, A., and Menczer, F. (2016). Botornot: A system to evaluate social bots. In Proceedings of the 25th International Conference Companion on World Wide Web, pages 273–274. International World Wide Web Conferences Steering Committee.
Ferrara, E., Varol, O., Davis, C., Menczer, F., and Flammini, A. (2016). The rise of social bots. Commun. ACM, 59(7):96–104.
Ho, T. K. (1995). Random decision forests. In Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on, volume 1, pages 278–282. IEEE.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8):1735–1780.
Kononenko, I. (1994). Estimating attributes: analysis and extensions of relief. In European conference on machine learning, pages 171–182. Springer.
LeCun, Y., Huang, F. J., and Bottou, L. (2004). Learning methods for generic object recognition with invariance to pose and lighting. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, volume 2, pages II–104. IEEE.
Lee, K., Eoff, B. D., and Caverlee, J. (2011). Seven months with the devils: A long-term study of content polluters on twitter.
Ratkiewicz, J., Conover, M., Meiss, M. R., Gonçalves, B., Flammini, A., and Menczer, F. (2011). Detecting and tracking political abuse in social media.
Rosenblatt, F. (1961). Principles of neurodynamics. perceptrons and the theory of brain mechanisms. Technical report, CORNELL AERONAUTICAL LAB INC BUFFALO NY.
Wang, G., Zhang, X., Tang, S., Zheng, H., and Zhao, B. Y. (2016). Unsupervised clickstream clustering for user behavior analysis. In SIGCHI Conference on Human Factors in Computing Systems.
Witten, I. H., Frank, E., Hall, M. A., and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Yang, Z., Wilson, C., Wang, X., Gao, T., Zhao, B. Y., and Dai, Y. (2014). Uncovering social network sybils in the wild. ACM Transactions on Knowledge Discovery from Data (TKDD), 8(1):2.
Zhang, X., Zhao, J., and LeCun, Y. (2015). Character-level convolutional networks for text classification. In Advances in neural information processing systems, pages 649–657.
Publicado
06/11/2017
Como Citar
BRAZ, Paulo A.; GOLDSCHMIDT, Ronaldo R..
Um Método para Detecção de Bots Sociais Baseado em Redes Neurais Convolucionais Aplicadas em Mensagens Textuais. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 17. , 2017, Brasília.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
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p. 501-508.
DOI: https://doi.org/10.5753/sbseg.2017.19524.