Intrusion detection systems face significant challenges, including the inability to detect unknown threats and imbalances between normal and anomalous traffic. To address these limitations, we propose a semi-supervised intrusion detection algorithm based on GAN with a Transformer backbone for network security in IoT devices. To address the issue of imbalanced normal and anomalous traffic due to the diversity of network behavior and the difficulty that supervised algorithms experience in detecting unknown intrusions, we use only normal traffic as training data. By integrating the self-attention mechanism of Transformers, we leverage their ability to capture long-range dependencies in sequential data, enhancing the core capability of the GAN. The experimental results show that our algorithm achieves an F1-score of 95.2% and a false omission rate (FOR) of 10.7% on the CIC-IDS2017 dataset. On the Kitsune dataset, it attains an F1-score of 83.2% and a FOR of 15.8%. In real-world applications, when the algorithm was deployed on actual vehicle devices, it maintained strong performance with a FOR of 13%, further validating the practical applicability and value of the algorithm.