Open Access

ARTICLE

Cyber Security Analysis and Evaluation for Intrusion Detection Systems

Yoosef B. Abushark¹, Asif Irshad Khan^1,*, Fawaz Alsolami¹, Abdulmohsen Almalawi¹, Md Mottahir Alam², Alka Agrawal³, Rajeev Kumar⁴, Raees Ahmad Khan³

1 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
3 Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, Uttar Pradesh, India
4 Department of Computer Science and Engineering, Babu Banarasi Das University, Lucknow, 226028, India

* Corresponding Author: Asif Irshad Khan. Email:

Computers, Materials & Continua 2022, 72(1), 1765-1783. https://doi.org/10.32604/cmc.2022.025604

Received 29 November 2021; Accepted 11 January 2022; Issue published 24 February 2022

Abstract

Machine learning is a technique that is widely employed in both the academic and industrial sectors all over the world. Machine learning algorithms that are intuitive can analyse risks and respond swiftly to breaches and security issues. It is crucial in offering a proactive security system in the field of cybersecurity. In real time, cybersecurity protects information, information systems, and networks from intruders. In the recent decade, several assessments on security and privacy estimates have noted a rapid growth in both the incidence and quantity of cybersecurity breaches. At an increasing rate, intruders are breaching information security. Anomaly detection, software vulnerability diagnosis, phishing page identification, denial of service assaults, and malware identification are the foremost cyber-security concerns that require efficient clarifications. Practitioners have tried a variety of approaches to address the present cybersecurity obstacles and concerns. In a similar vein, the goal of this research is to assess the idealness of machine learning-based intrusion detection systems under fuzzy conditions using a Multi-Criteria Decision Making (MCDM)-based Analytical Hierarchy Process (AHP) and a Technique for Order of Preference by Similarity to Ideal-Solutions (TOPSIS). Fuzzy sets are ideal for dealing with decision-making scenarios in which experts are unsure of the best course of action. The projected work would support practitioners in identifying, prioritising, and selecting cybersecurity-related attributes for intrusion detection systems, allowing them to design more optimal and effective intrusion detection systems.

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Keywords

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