Machine Learning Approaches for Cybersecurity Threat Detection and Mitigation
Keywords:
machine learning, cybersecurity, detection, mitigation.Abstract
The critical role of cybersecurity in modern society is underscored by this manuscript, which explores the increasing significance of cybersecurity in the contemporary digital environment. It acknowledges the ongoing need for cybersecurity frameworks that are resilient in the face of the evolving nature of emergent threats and technological advancements The study highlights the advantageous use of machine learning in enhancing cybersecurity frameworks. A range of approaches is established to attain these aims, thereby enabling a comprehensive assessment of machine learning models. The key issues studied are the classification of threats, optimization methodologies for machine learning models tailored for cybersecurity, and implementation methods. The effectiveness of various machine learning algorithms is evaluated using theoretical frameworks and practical case studies, providing insights aimed at improving cybersecurity procedures. The aim of the research is to demonstrate the effectiveness of various machine learning techniques in meeting specific needs. The book examines methods to enhance the accessibility of cybersecurity and machine learning by concentrating on contemporary advancements in these fields. Visual comparisons are employed to enhance understanding. This document summarizes findings and viewpoints about the capacity of various machine learning approaches to enhance information system security, while also considering prospective future developments. This research aims to engage diverse audiences, promoting discussions and insights that span several fields and competence levels.
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Copyright (c) 2025 Ali Hasan Kamil
This work is licensed under a Creative Commons Attribution 4.0 International License.
