Dynamic ML Model Updating Strategies for Detection of Evolving Network Attacks

Paper in proceeding, 2025

Attack detection plays a crucial role in managing the security of communication networks. Thus, advanced methods have been developed to tackle this problem. However, the need for more flexible and adaptable detection systems remains, able to generalize across various attack scenarios and retain model performance over time. In this paper, we address these challenges by proposing two versions of a continuously updating attack detector in the form of binary classifier. Experimental evaluation on diverse datasets and various base classifiers confirms the effectiveness of our methodology.