The increasing sophistication of cyberattacks has escalated the need for advanced network intrusion detection systems (NIDS). This thesis presents TL-SOINN, an innovative algorithm combining Deep Transfer Learning with a Self-Organizing Incremental Neural Network (SOINN). TL-SOINN leverages the strengths of both approaches, enabling effective learning from diverse datasets and rapid adaptation to new attack types. The model uses a Convolutional Neural Network (CNN) trained on a comprehensive dataset of cyberattacks to extract key features, which are then processed by SOINN. This method allows the system to retain knowledge of known threats while continuously adapting to new ones. Experimental results demonstrate that TL-SOINN outperforms traditional methods in detecting a broad spectrum of cyberattacks, making it a robust solution for evolving network security needs. Additionally, the thesis integrates the Suricata tool to enrich the dataset, enhancing the model’s generalization capabilities across different network environments. The successful application of TL-SOINN across various datasets highlights its potential as an effective and adaptable NIDS solution, capable of improving cybersecurity in increasingly complex and dynamic systems.