Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-10, Vol.23 (20), p.8642
• Main Authors: Ramzan, Mahrukh, Shoaib, Muhammad, Altaf, Ayesha, Arshad, Shazia, Iqbal, Faiza, Castilla, Ángel Kuc, Ashraf, Imran
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; Back propagation; Cloud computing; Data mining; Datasets; Deep learning; denial of service attack detection; Denial of service attacks; Detectors; Discriminant analysis; distributed denial of service attacks; Experiments; Feature selection; Global positioning systems; GPS; Internet of Things; Machine learning; Middleware; network security; Neural networks; Radio frequency identification; Support vector machines; Trends
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23208642

Internet security is a major concern these days due to the increasing demand for information technology (IT)-based platforms and cloud computing. With its expansion, the Internet has been facing various types of attacks. Viruses, denial of service (DoS) attacks, distributed DoS (DDoS) attacks, code injection attacks, and spoofing are the most common types of attacks in the modern era. Due to the expansion of IT, the volume and severity of network attacks have been increasing lately. DoS and DDoS are the most frequently reported network traffic attacks. Traditional solutions such as intrusion detection systems and firewalls cannot detect complex DDoS and DoS attacks. With the integration of artificial intelligence-based machine learning and deep learning methods, several novel approaches have been presented for DoS and DDoS detection. In particular, deep learning models have played a crucial role in detecting DDoS attacks due to their exceptional performance. This study adopts deep learning models including recurrent neural network (RNN), long short-term memory (LSTM), and gradient recurrent unit (GRU) to detect DDoS attacks on the most recent dataset, CICDDoS2019, and a comparative analysis is conducted with the CICIDS2017 dataset. The comparative analysis contributes to the development of a competent and accurate method for detecting DDoS attacks with reduced execution time and complexity. The experimental results demonstrate that models perform equally well on the CICDDoS2019 dataset with an accuracy score of 0.99, but there is a difference in execution time, with GRU showing less execution time than those of RNN and LSTM.