A hybrid machine and deep learning model for detecting DDoS attacks

Abstract

Over the past decades, distributed denial of service attacks have been one of the most devastating security threats, disrupting many services that rely heavily on the Internet and leading to significant economic losses for various sectors. Identifying DDoS attacks poses a significant challenge that must be addressed through detection methods before effective mitigation strategies can be deployed. Detection of these attacks requires sophisticated technical solutions to discern malicious traffic from legitimate network activity in real-time. According to the work in this paper, a hybrid model combining Machine Learning and Deep Learning algorithms is introduced to enhance the detection of DDoS attacks. This hybrid model employs two main stages. The Gradient Boosting (GB) Machine Learning algorithm has been utilized in the first stage for efficient feature selection and computational complexity reduction. In the second stage, the Gated Recurrent Units (GRU) Deep learning algorithm has been employed to improve attack detection by capturing temporal dependencies and complex patterns. The hybrid model (GB-GRU) capitalizes on the strengths of both traditional machine learning and deep learning algorithms. The proposed hybrid model’s effectiveness is validated using the CICDoS2019 dataset, showing promising results in DDoS attack detection scenarios. Experimental results indicate that the proposed hybrid model achieves high detection performance with an accuracy rate of 99.96%, a False Positive Rate (FPR) of 0.2, less computational complexity compared to existing algorithms, and a test time of 5.729s.