Optimizing Network Security with Machine Learning and Multi-Factor Authentication for Enhanced Intrusion Detection - Mahmood, Rafah Kareem; Mahameed, Ans Ibrahim; Lateef, Noor Q.; Jasim, Hasanain M.; Radhi, Ahmed Dheyaa; Ahmed, Saadaldeen Rashid; Tupe-Waghmare, Priyanka (Journal of Robotics and Control (JRC), 2024)
This study examines the utilization of machine learning methodologies and multi-factor authentication (MFA) to bolster network security, specifically targeting network intrusion detection. We analyze the way in which the integration of these technologies effectively tackles existing security concerns and constraints. The research highlights the importance of incorporating energy conservation and environmental impact reduction into security solutions, in addition to traditional cryptography and biometric methods. In addition, we tackle the limitations of centralized systems, such as vulnerabilities to security breaches and instances of system failures. The study examines different security models, encompassing categories, frameworks, consensus protocols, applications, services, and deployment goals in order to determine their impact on network security. In addition, we offer a detailed comparison of seven machine learning models, showcasing their effectiveness in enhancing network intrusion detection and overall security. The objective of this study is to provide in-depth understanding and actionable suggestions for utilizing machine learning with MFA (Multi-Factor Authentication) to enhance network defensive tactics.