Cyber Threat Prediction and Assessment with Machine Learning Approaches

Cyber threats are malevolent activities designed to compromise computer systems and steal sensitive information, often targeting individuals and, increasingly, a wide range of organizations. To address the issue, the study explores various machine learning approaches for the predictive analysis and assessment of cyber threats. So, various machine learning algorithms provide effective approaches to addressing the security issues by using data-driven techniques for cyber threat prediction and assessment, aiming to make an impactful contribution to the research field. Machine learning algorithms are highly effective for solving problems by quickly making decisions, detecting various types of attacks, and predicting the likelihood of cyber attacks on the network. Furthermore, the challenges of adopting machine learning algorithms in cyber security are explored, including data privacy concerns, understanding how the models function, and protection against cyber attacks. Machine learning approach enhances security protection and prevention by detecting cyber threats early and automatically responding to them. Over time, it gets better at handling new threats. So the aim of the paper is to present a framework for cyber threat prediction and provide a comprehensive overview of the state-of-the-art techniques in cyber threat assessment using machine learning algorithms. Additionally, the paper aims to offer guidance and a reference framework for future research in cyber threat prediction and assessment in the field of cyber security.