Abstract
Machine learning (ML) is one of the dominating technologies practiced in both the industrial and academic domains throughout the world. ML algorithms can examine the threats and respond to intrusions and security incidents swiftly in an instinctive way. It plays a critical function in providing a proactive security mechanism in the cybersecurity domain. Cybersecurity ensures the real time protection of information, information systems, and networks from intruders. Several security and privacy reports have cited that there has been a rapid increase in both the frequency and the number of cybersecurity breaches in the last decade. Information security has been compromised by intruders at an alarming rate. Anomaly detection, phishing page identification, software vulnerability diagnosis, malware identification, and denial of services attacks are the main cyber-security issues that demand effective solutions. Researchers and experts have been practicing different approaches to address the current cybersecurity issues and challenges. However, in this research endeavor, our objective is to make an idealness assessment of machine learning-based intrusion detection systems (IDS) under the hesitant fuzzy (HF) conditions, using a multi-criteria decision making (MCDM)-based analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal-solutions (TOPSIS). Hesitant fuzzy sets are useful for addressing decision-making situations in which experts must overcome the reluctance to make a conclusion. The proposed research project would assist the machine learning practitioners and cybersecurity specialists in identifying, selecting, and prioritizing cybersecurity-related attributes for intrusion detection systems, and build more ideal and effective intrusion detection systems.