Menyu
ITI
əməkdaslarının elmi isləri
Elektron kitabxana
Konfranslar İnformasiya Sistemi
Qəzetlər
UOT 004
|
ITI əməkdaşlarının elmi işləri - məqalə |
Biblioqrafik təsvir | Alguliyev , R.M. The Improved LSTM and CNN Models for DDoS Attacks Prediction in Social Media / R.M. Alguliyev , R.M. Aliguliyev , F.D. Abdullayeva // International Journal of Cyber Warfare and Terrorism. - 2019. - N: vol 9, no 1..- P. 1-18. | Annotasiya | Automatic identification of conversations related to DDoS events in social networking logs helps the
organizations act proactively through early detection of negative and positive sentiments in cyberspace.
In this article, the authors describe the novel application of a deep learning method to the automatic
identification of negative and positive sentiments in large volumes of social networking texts. The
authors present classifiers based on Convolutional Neural Network (CNN) and Long Short-Term
Memory (LSTM) to address this problem domain. The improved CNN and LSTM architecture
outperform the classification techniques that are common in this domain including classic CNN and
classic LSTM in terms of classification performance, which is measured by recall, precision, f-measure,
train loss, train accuracy, test loss, and test accuracy. In order to predict the occurrence probability
of the DDoS events the next day, the negative and positive sentiments in social networking texts
are used. To verify the efficacy of the proposed method experiments is conducted on Twitter data. | Elektron variant | Elektron variant |
|
________
© ict.az http://ict.az/az
|
|