Evaluation of Intrusion Detection Systems in Cyber Security using Fuzzy OffLogic and MCDM Approach

Modern cybersecurity infrastructures rely heavily on Intrusion Detection Systems (IDS) to detect and prevent malicious activities and unauthorized access. Given the growing complexity of network topologies and the rising frequency of cyber threats, evaluating IDS solutions requires a systematic and unbiased approach. In this study, thirteen widely used IDS models are assessed using a multi-criteria evaluation framework across four key dimensions: detection accuracy, resource efficiency, scalability, and false positive rate. The goal is to support informed, datadriven decision-making for stakeholders such as policymakers, IT administrators, and security analysts when selecting an appropriate IDS. The VIKOR method is employed to rank the IDS alternatives based on the assigned weights, while Fuzzy OffLogic is applied to integrate expert assessments expressed as intervals. The results reveal that modern AI-based IDS models demonstrate strong performance in scalability and resource utilization, and they outperform traditional systems in adaptability and detection accuracy.