Machine Learning-Driven Mitigation Protocols in Advanced Cybersecurity Systems
Moshood Yussuf, Olubusayo Mesioye, Adedeji Olaniyi Lamina, Gerald Nwachukwu, Tunde Ohiozua · International Journal of Research Publication and Reviews · 2024
Traditional cybersecurity methods are becoming less effective against sophisticated and adaptable cyber threats in the quickly changing digital ecosystem.A move towards more sophisticated mitigation strategies powered by machine learning has resulted from this.By utilising data-driven insights, ML has the potential to improve cybersecurity systems by improving the efficiency and accuracy of threat detection, analysis, and response.This article examines how machine learning can be used into cybersecurity mitigation strategies and emphasises how revolutionary this approach can be.We start by looking at the shortcomings of traditional cybersecurity strategies, which frequently find it difficult to keep up with the ever-evolving nature of contemporary threats.We next explore the several machine learning approaches being used to tackle these problems, including automatic response systems, behavioural analysis, and anomaly identification. By means of a review of recentWe demonstrate how ML may greatly enhance threat detection and response capabilities through developments and case studies.The use of machine learning in cybersecurity is not without difficulties, despite its benefits.Careful management is required of issues like data quality, model accuracy, and the possibility of adversarial attacks.In order to strengthen cybersecurity defences, this article also addresses the integration of machine learning with other new technologies and future directions in this field of study.This article intends to shed light on the crucial role of machine learning in enhancing cybersecurity procedures and preparing for the next generation of cyber threats by offering a thorough analysis of existing practices and future prospects. Keywords;