A real-time evaluation framework for machine learning-based ids

Abstract

With the rapid evolution of internal and external cyber threats, building a reliable security management system has become an urgent demand to mitigate system risks. In such systems, the Intrusion Detection System (IDSs) and Intrusion Prevention Systems (IPSs) are central components widely deployed to prevent malicious traffic from attackers. Most of the research target to enhance the performance of IDSs and IPSs. One problem that affects the performance is training datasets, and the solution to resolve this problem use benchmark datasets. However, there are many problems with that solution. Firstly, many valuable datasets used for evaluating the IDS model are internal and cannot be shared due to privacy issues. Secondly, open-source datasets such as DEFCON, KDD, CAIDA have its limitation and do not reflect the current world trends. In this paper, we introduce a framework used for practically evaluating …