Narrow Results

The following topics are under this section:

• The future of predicting lifestyle diseases is here in Asia
• Breaking Barriers for Artificial Intelligence (AI) in Healthcare: bridging vision and reality with the language of trust
• Overcoming Challenges of Managing Information in Life Sciences, Towards the Digital Future
• Under the Weather: Cybersecurity Woes in the healthcare Industry

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.