A Comprehensive Study on Distributed Denial of Service Attack Detection using Deep Learning Technique

Distributed Denial of Service (DDoS) attacks are among the most dangerous threats in cyberspace impacting areas such as the Internet of Things (IoT), Cybersecurity, and Cloud Computing, which leads to network interruptions. DDoS attacks target IoT environments by manipulating IoT networks, consuming resources and interrupting services, leading to privacy challenges and performance degradation. However, accurate detection of DDoS attacks is difficult, due to their various DDoS attack types, heterogeneity of network security and complexity of communication protocols. Advanced Deep Learning (DL) approaches have been employed for effective detection of DDoS attacks while reducing false-positive rates and improving high detection rates of the different network traffic patterns. The DL algorithms such as Convolutional Neural Networks (CNN), Bidirectional Long-Short Term Memory (Bi-LSTM), Recurrent Neural Networks (RNN), and Deep Neural Networks (DNN) are used for the detection of DDoS attacks. This survey focuses on analyzing the advantages and limitations of different DL algorithms used for DDoS attack detection. The performance of the DL models is evaluated based on performance metrics such as accuracy, precision, recall, and F1-score.