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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.<\/p>","DOI":"10.4018\/ijcwt.2019010101","type":"journal-article","created":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T09:26:21Z","timestamp":1552037181000},"page":"1-18","source":"Crossref","is-referenced-by-count":11,"title":["The Improved LSTM and CNN Models for DDoS Attacks Prediction in Social Media"],"prefix":"10.4018","volume":"9","author":[{"given":"Rasim M.","family":"Alguliyev","sequence":"first","affiliation":[{"name":"Institute of Information Technology, Azerbaijan National Academy of Sciences, Baku, Azerbaijan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9795-1694","authenticated-orcid":true,"given":"Ramiz M.","family":"Aliguliyev","sequence":"additional","affiliation":[{"name":"Institute of Information Technology, Azerbaijan National Academy of Sciences, Baku, Azerbaijan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2288-6255","authenticated-orcid":true,"given":"Fargana J","family":"Abdullayeva","sequence":"additional","affiliation":[{"name":"Institute of Information Technology, Azerbaijan National Academy of Sciences, Baku, Azerbaijan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJCWT.2019010101-0","unstructured":"Bleakley, K., & Vert, J. 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