This paper explores a novel method in computer security by improving network threat detection through transfer learning. We created a model that effectively recognizes possible dangers by analyzing network behavior using transfer learning. Our model is a reliable tool for detecting network threats because of its remarkable high accuracy and low error rates. Our tests demonstrated the model's great efficacy, consistently improving its ability to recognize threats with few errors. This is an important improvement for network security since it shows that transfer learning is an effective technique, particularly in situations where there is a lack of available data for learning. Our work is important because it can lead to better online security. Our demonstration of the effective application of transfer learning in this domain opens up new avenues for cybersecurity research and development in the future. In today's digital environment, where threats are continually evolving, research like this is extremely crucial for combating them.