Cyber Security Threat Detection Using Machine Learning

The present review delves into the development and effectiveness of machine learning (ML) methods in the context of cybersecurity threat identification. More sophisticated and adaptable solutions are required as cyber threats increase in number and complexity beyond what can be achieved with standard security techniques. This study provides a thorough assessment of the several machine learning (ML) techniques that have been used to identify and categorize cyber risks. These algorithms include supervised, unsupervised, and deep learning approaches. We assess these methods' efficacy in identifying malware, phishing, and network intrusions while emphasizing their advantages and disadvantages. While ML offers considerable gains over traditional approaches, comparative study shows that issues like algorithmic bias, data quality, and adaptation to changing threats still exist. In an effort to improve predictive capabilities and real-time threat response, the evaluation also highlights trends and future directions in the integration of machine learning with cybersecurity. In order to implement ML-driven security systems, academics and practitioners may use this paper as a thorough guide. Keywords—Cybersecurity, Machine Learning, Algorithms, Threats, Security.