This paper addresses the critical need for robust network intrusion detection systems (NIDS) in the face of escalating cyber threats. The authors propose a binary classification framework using TabTransformer, a transformer-based architecture, to enhance current methodologies. The study leverages a dataset derived from a simulated military network environment, which emulates the complexities of a typical US Air Force LAN. The TabTransformer model is designed to handle both categorical and numerical features, effectively capturing intricate patterns and dependencies within tabular data. The paper details the methodology, including data preprocessing, model architecture, and evaluation metrics, and presents empirical results demonstrating the efficacy of the TabTransformer approach in mitigating cyber threats and enhancing network security. The TabTransformer model outperforms traditional models like SVM, LR, MLP, and a Voting Model, achieving the highest F1-score of 98.45%. The findings highlight the importance of advanced machine learning models in network intrusion detection and the need for continuous advancements in intrusion detection systems to keep pace with evolving cyber threats.This paper addresses the critical need for robust network intrusion detection systems (NIDS) in the face of escalating cyber threats. The authors propose a binary classification framework using TabTransformer, a transformer-based architecture, to enhance current methodologies. The study leverages a dataset derived from a simulated military network environment, which emulates the complexities of a typical US Air Force LAN. The TabTransformer model is designed to handle both categorical and numerical features, effectively capturing intricate patterns and dependencies within tabular data. The paper details the methodology, including data preprocessing, model architecture, and evaluation metrics, and presents empirical results demonstrating the efficacy of the TabTransformer approach in mitigating cyber threats and enhancing network security. The TabTransformer model outperforms traditional models like SVM, LR, MLP, and a Voting Model, achieving the highest F1-score of 98.45%. The findings highlight the importance of advanced machine learning models in network intrusion detection and the need for continuous advancements in intrusion detection systems to keep pace with evolving cyber threats.