An integrated Neutrosophic-BWM-COPRAS based MCDM framework for intrusion detection system software selection

Intrusion Detection Systems (IDS) play a significant role in cybersecurity as they detect potential threats and protect network infrastructure. With the existing cybersecurity environment, the selection of the most suitable IDS software tool is very crucial to provide strong cybersecurity practices in organizations. The availability of various IDS software tools with varying features and capabilities makes the selection of the optimal one a complex decision-making problem. Conventional multi-criteria Decision making (MCDM) approaches cannot deal with the uncertainty and ambiguity associated with these criteria, which can result in making wrong decisions. The current study introduces a novel framework for the selection of an optimal IDS software tool that integrates Neutrosophic sets and COCOSO (Combined Compromise Solution) to effectively deal with the uncertainty involved in IDS selection. The Best-Worst Method considers the best and worst criteria for consistent and accurate weight assignment to improve decision accuracy. The Neutrosophic approach deals with ambiguity by considering truth, falsity, and indeterminacy together, and improves the flexibility of the COCOSO approach to assess these IDS software tools. A case study illustrates the usability and efficiency of the introduced framework to select an IDS software tool in secure dynamic environments over conventional approaches.