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Biblioqrafik təsvir | Alguliyev , R.M. Deep Learning Method for Prediction of DDoS Attacks on Social Media / R.M. Alguliyev , R.M. Aliguliyev , F.D. Abdullayeva // Advances in Data Science and Adaptive Analysis. - 2019. - N: Vol. 11(5) Nos. 1 & 2.- P. 1950002-1-1950002-19. | Annotasiya | Recently, data collected from social media enable to analyze social events and make
predictions about real events, based on the analysis of sentiments and opinions of users.
Most cyber-attacks are carried out by hackers on the basis of discussions on social
media. This paper proposes the method that predicts DDoS attacks occurrence by finding
relevant texts in social media. To perform high-precision classification of texts to positive
and negative classes, the CNN model with 13 layers and improved LSTM method are
used. In order to predict the occurrence of the DDoS attacks in the next day, the negative
and positive sentiments in social networking texts are used. To evaluate the efficiency
of the proposed method experiments were conducted on Twitter data. The proposed
method achieved a recall, precision, F-measure, training loss, training accuracy, testing
loss, and test accuracy of 0.85, 0.89, 0.87, 0.09, 0.78, 0.13, and 0.77, respectively | Elektron variant | Elektron variant |
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