Abstract: With the rapid development of the Internet， DDoS attacks have become a major challenge in the field of network security. DDoS attacks disrupt the normal operation of target servers by controlling a large number of distributed computers to send massive amounts of malicious requests， seriously affecting the stability and security of network services. Traditional DDoS attack detection methods， such as rule-based detection， statistical methods， and machine learning approaches， often face issues such as high false positive rates and low detection efficiency when dealing with complex and dynamically changing network traffic. To address these issues， this paper proposes a Transformer-based DDoS attack detection system. The system utilizes the powerful self-attention mechanism of the Transformer model to capture long-term dependencies in network traffic， enabling more accurate identification of abnormal traffic patterns. Additionally， by incorporating positional encoding， the system can better handle temporal information and enhance the model’s ability to perceive global network traffic. Experimental results on datasets show that the Transformer-based DDoS detection model significantly outperforms comparison methods in terms of detection accuracy and recall rate， demonstrating the effectiveness of the proposed approach. 

Key words:  , DDoS attack detection, Transformer model, self attention mechanism, network security