A Novel Pipeline to Recommend Intrusion Detection Systems Configurations

Abstract

Intrusion Detection Systems (IDS) are becoming increasingly important to provide a certain level of safety in a variety of complex environments. An IDS’s proper operation is dependent on the quality of the subjective measurements that influence its quality. All steps of the IDS development life cycle must be covered to increase quality. The design phase of such a system may take long enough to show its evolution. Furthermore, each provided IDS model has a level of precision that is frequently related to the state of the system that needs to be examined. To avoid this problem, it is necessary to guide the designer in selecting suitable models and tests. In light of this, a dedicated framework has been proposed to recommend IDS instance configurations. This scope combines clustering and classification techniques to produce a resilient instance IDS analysis that ensures good independent performance from system variations. The study’s findings demonstrate that combining approaches resulted in consistent performance and high prediction accuracy.