CROSS-SECURE: A Domain-Adaptive IDS with Real- Time Web Interface

In order to overcome the problems and the difficulties that modern networks are currently facing, flexibility is just as essential as algorithms. Even though intrusion detection systems (IDS) have become more effective with the inclusion of machine learning (ML) techniques, most of the models used in machine learning have an underlying major problem in that they can only work in the environment or domain in which they are trained. The aim of this study is to overcome these socalled ”domain shift” problems. However, instead of relying on traditional approaches, which cannot generalize, such as SVM or Decision Trees, we are proposing a domainadaptive model that was trained on several data sets. With the help of domain adaptation, we introduce you to CROSSSECURE, a system that not only provides robustness but allows users to visualize these insights through a real-time online interface. This research provides a solution that can be adapted to the networks of today by bridging the gap between the accuracy of models and practicality.