Network intrusion detection systems are essential for defending recent computer networks from ever-evolving cyber attacks. Security is of utmost importance due to the complex and constantly changing nature of network threats. To improve the detection capabilities in network traffic, this research presents a unique method for intrusion detection by utilizing attention-based Transformer architectures. The proposed Transformer-based model offers an adaptable and reliable method for detecting sophisticated and dynamic threats by fusing the strength of the self-attention mechanism. The model is evaluated on two network intrusion benchmark datasets (NSL-KDD and UNSW-NB15). The correlation technique is used for feature extraction, and both binary and multiclass classification with and without feature extraction are performed on the datasets. The proposed model achieved over 99% accuracy, precision, and recall on the two datasets. The experimental results indicate that the proposed approach provides better results than other systems.