This paper presents a novel approach for network intrusion detection using TabTransformer, a transformer-based architecture designed for tabular data. The study addresses the critical need for robust network intrusion detection systems (NIDS) in the face of increasing cyber threats. A dataset derived from a simulated military network environment is used to explore various intrusion scenarios encountered in cyber warfare. The research reviews existing methodologies, including anomaly-based and deep learning approaches, and proposes a binary classification framework using TabTransformer to enhance current intrusion detection techniques. TabTransformer leverages the self-attention mechanism to effectively capture intricate patterns and dependencies within tabular data, making it particularly well-suited for network intrusion detection. The model processes both categorical and numerical features, with categorical features transformed into dense embeddings and numerical features processed through fully connected layers. The combined embeddings are then passed through a binary classification layer to distinguish between "Normal" and "Anomalous" network connections. The study evaluates the performance of TabTransformer against traditional models such as SVM, LR, MLP, and a Voting Model. Results show that TabTransformer achieves the highest F1-score of 98.45%, demonstrating its superior effectiveness in network intrusion detection. The model's ability to handle both categorical and numerical features, along with its capacity to capture intricate patterns in tabular data, positions it as a powerful tool for detecting and mitigating cyber threats in real-time. The research also explores the potential of large language models (LLMs) in enhancing network intrusion detection by providing enhanced contextual understanding of network traffic data and associated logs. The integration of LLMs could further improve the accuracy and efficiency of intrusion detection systems. The study concludes that TabTransformer is a promising solution for addressing the challenges of network intrusion detection in an increasingly interconnected world. Its superior performance and scalability make it a leading choice for mitigating cyber threats and enhancing network security. The findings highlight the importance of continuously advancing intrusion detection systems to keep pace with the evolving threat landscape.This paper presents a novel approach for network intrusion detection using TabTransformer, a transformer-based architecture designed for tabular data. The study addresses the critical need for robust network intrusion detection systems (NIDS) in the face of increasing cyber threats. A dataset derived from a simulated military network environment is used to explore various intrusion scenarios encountered in cyber warfare. The research reviews existing methodologies, including anomaly-based and deep learning approaches, and proposes a binary classification framework using TabTransformer to enhance current intrusion detection techniques. TabTransformer leverages the self-attention mechanism to effectively capture intricate patterns and dependencies within tabular data, making it particularly well-suited for network intrusion detection. The model processes both categorical and numerical features, with categorical features transformed into dense embeddings and numerical features processed through fully connected layers. The combined embeddings are then passed through a binary classification layer to distinguish between "Normal" and "Anomalous" network connections. The study evaluates the performance of TabTransformer against traditional models such as SVM, LR, MLP, and a Voting Model. Results show that TabTransformer achieves the highest F1-score of 98.45%, demonstrating its superior effectiveness in network intrusion detection. The model's ability to handle both categorical and numerical features, along with its capacity to capture intricate patterns in tabular data, positions it as a powerful tool for detecting and mitigating cyber threats in real-time. The research also explores the potential of large language models (LLMs) in enhancing network intrusion detection by providing enhanced contextual understanding of network traffic data and associated logs. The integration of LLMs could further improve the accuracy and efficiency of intrusion detection systems. The study concludes that TabTransformer is a promising solution for addressing the challenges of network intrusion detection in an increasingly interconnected world. Its superior performance and scalability make it a leading choice for mitigating cyber threats and enhancing network security. The findings highlight the importance of continuously advancing intrusion detection systems to keep pace with the evolving threat landscape.