Detection Systems for Distributed Denial-of-Service (DDoS) Attack Based on Time Series: A Review
Ahmed Adil Nafea, Mustafa Maad Hamdi, Baraa Saad Abdulhakeem, Ahmed Thair Shakir, Mustafa S. Ibrahim Alsumaidaie, Ali Muwafaq Shaban · 2024
The Distributed Denial-of-Service (DDoS) attacks are one of the most critical threats to the stability and security of the Internet. With the increasing number of devices connected to the Internet, the frequency and severity of DDoS attacks are also increasing. To mitigate the impact of DDoS attacks, intelligent detection systems are becoming increasingly important. This paper reviews the recent literature on intelligent techniques, including machine learning (ML), Deep Learning (DL), and artificial intelligence (AI), for detecting DDoS attacks. We will provide an overview of the existing research in the field and analyse the trends in using time series data analysis for DDoS attack detection. A taxonomy and conceptual framework for DDoS mitigation are presented. This study highlights the use of several intelligent techniques for detecting DDoS attacks and evaluates the performance utilizing real datasets and also discusses future research directions in this field.