An Attention-Based Intrusion Detection System with Incremental Online Learning Approach for Unmasking Evolving Cyber Threats

Traditional intrusion detection systems (IDS) often struggle to keep pace with the dynamic nature of network environments, where a massive number of packets is exchanged daily, and new attack methods emerge rapidly due to advancements in technologies and applications. Conventional IDS typically rely on static data analysis, which poses challenges in promptly identifying novel threats. To address these challenges, we propose a framework for IDS leveraging Transformer-MLP architecture integrated with incremental online learning during the training process. Unlike traditional methods that require retraining the entire model with each new dataset, incremental online learning enables the model to assimilate new information while retaining previously acquired knowledge. This approach significantly reduces downtime during training and alleviates the need for extensive data storage capacity. Our evaluation demonstrates the effectiveness of this approach in swiftly identifying new attack patterns. By combining the power of Transformer-MLP with incremental learning, our framework empowers IDS to adapt to evolving threats in real-time, enhancing the overall security posture.