Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3042 2019 2019 Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia Bashar Bashar Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia Siti Hajar Othman 2Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Kuala Terengganu 21300, Malaysia Shukor Abd Razak Nottingham Trent University, Nottingham, UK Alexandros Konios Due to the increasing digitization of city processes, there has been a significant shift in how cities are governed and how people make their living. However, several types of attacks could target smart cities, and Flooding Attacks (FA) are the most dangerous type. It is also a major issue for many people and programs using the Internet nowadays. Security in smart cities refers to preventative measures necessary to shield the city and its residents from direct or indirect harm by attackers who try to crash the system and deny legitimate users the use of the services. Smart city security, in contrast to standard security mechanisms, necessitates new and creative approaches to protecting the systems and applications while considering characteristics like resource limitations, distributed architecture nature, and geographic distribution. Smart cities are vulnerable to several particular issues, including faulty communication, insufficient data, and privilege protection. Therefore, a hybrid CRNN model that consists of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) algorithms is employed for the detection of Flood Attacks based on the classification of traffic data. Subsequently, the performance of the CRNN is tested and evaluated using the CIC-Bell-DNS-EXF-2021 dataset. The obtained accuracy results of the proposed CRNN model achieved in FA detection is 99.2%. 2024 2024 311 322 10.54216/JCIM.140222 https://www.americaspg.com/articleinfo/2/show/3042