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Intrusion detection systems play a pivotal role in safeguarding computer networks from a plethora of cyber threats. Traditional methods have demonstrated effectiveness, but the evolving nature of attacks demands novel approaches that can capture intricate patterns and relationships within network data. In this paper, we propose a groundbreaking CNN-Transformer hybrid deep learning model for Network Intrusion Detection Systems (NIDS) prediction, utilizing the Canadian Institute of Cyber Security dataset. The hybrid architecture capitalizes on the strengths of both Convolutional Neural Networks (CNNs) and Transformers. CNNs excel at capturing spatial features in data, making them suitable for identifying local patterns in network traffic. On the other hand, Transformers are adept at capturing global contextual relationships, thereby handling complex temporal dependencies in network sequences. By fusing these two powerful architectures, we achieve a comprehensive model capable of discerning both local anomalies and global attack trends. Our model is extensively evaluated on the Canadian Institute of Cyber Security dataset, and the results are nothing short of remarkable. We achieve an unprecedented accuracy of 99.4%, showcasing the efficacy of the proposed hybrid approach in the context of real-world network traffic. Furthermore, the model demonstrates a robust ability to generalize across diverse attack scenarios, effectively minimizing false positives and false negatives. As cyber threats continue to evolve, the significance of innovative models that offer superior detection accuracy and robust generalization cannot be overstated. This work not only furthers the field of intrusion detection but also underscores the potential of hybrid deep learning architectures in addressing complex cybersecurity challenges.