Machine Learning–Driven Analytical Framework for Detecting Cybersecurity Threats and System Vulnerabilities

Abstract

Organisations are becoming far more vulnerable to cybersecurity risks due to the quick development of digital technology, cloud computing, and networked systems. Traditional security measures, such as intrusion detection systems based on signatures and rule-based firewalls are no longer adequate to deal with complex and changing threat patterns.

Examining a large amount of network data and identifying patterns linked to negative events can be accomplished with machine learning. What we do is not the same as machine learning. Models for machine learning can appear at what happened in the past and use that to find things that're not normal. This helps us find risks that we know about and ones that we do not know about with machine learning.

A machine learning-based analytical approach for identifying cybersecurity risks and vulnerabilities in network environments is presented in this study. The suggested system incorporates several phases, such as feature engineering, preprocessing, data collecting, and classification through algorithms for supervised learning. Evaluation of experiments employing Benchmark datasets show more detection accuracy and fewer false alarm rates as compared to conventional systems.

The presented framework helps formulate intelligent, proactive, and flexible cyber security systems, which can address new risks in modern digital infrastructure.