This paper proposes a Transformer-based network intrusion detection method for cloud security. The method leverages the attention mechanism of the Transformer model to enhance the detection of network intrusion behaviors. The algorithm is designed to analyze the relationships between input features and intrusion types, thereby improving detection accuracy. The model is evaluated on the CIC-IDS 2018 dataset, achieving an accuracy of over 93%, which is comparable to the CNN-LSTM model. The algorithm is suitable for cloud environments and can adapt to different network conditions through parameter adjustment. The model is implemented with a focus on data preprocessing, model training, and prediction. The results show that the Transformer-based approach is effective in detecting network intrusions, particularly in cloud environments. The study also discusses the potential for integrating Graph Neural Networks into the model to improve its ability to detect complex intrusion patterns in distributed environments. The paper concludes that the proposed method is a viable solution for enhancing cloud security through network intrusion detection.This paper proposes a Transformer-based network intrusion detection method for cloud security. The method leverages the attention mechanism of the Transformer model to enhance the detection of network intrusion behaviors. The algorithm is designed to analyze the relationships between input features and intrusion types, thereby improving detection accuracy. The model is evaluated on the CIC-IDS 2018 dataset, achieving an accuracy of over 93%, which is comparable to the CNN-LSTM model. The algorithm is suitable for cloud environments and can adapt to different network conditions through parameter adjustment. The model is implemented with a focus on data preprocessing, model training, and prediction. The results show that the Transformer-based approach is effective in detecting network intrusions, particularly in cloud environments. The study also discusses the potential for integrating Graph Neural Networks into the model to improve its ability to detect complex intrusion patterns in distributed environments. The paper concludes that the proposed method is a viable solution for enhancing cloud security through network intrusion detection.