Advancing Cybersecurity through Machine Learning: Bridging Gaps, Overcoming Challenges, and Enhancing Protection

Abstract

The greatest technical achievement of the twenty-first century is machine learning (ML). The application of machine learning to detect cybersecurity vulnerabilities is a significant advancement in information security. A void exists in the field since the widespread application of machine learning technologies in cybersecurity remains distant. The primary cause of this gap is that contemporary technology has rendered it challenging for people to comprehend the role of machine learning in cybersecurity. The review seeks to furnish readers with a comprehensive analysis of machine learning's relevance across several facets of information security, especially for individuals interested in cybersecurity. It highlights the benefits of machine learning compared to human-operated detection methods and the diverse cybersecurity tasks it can do. This research elucidates various fundamental issues that impact real-world machine learning applications in cybersecurity. Ultimately, it examines how diverse businesses might advance machine learning in cybersecurity in the future, as this is crucial for the field's further growth. This study analyzes the contribution of machine learning to the enhancement of cybersecurity, highlighting the necessity of safeguarding sensitive information from theft and loss, as well as protecting critical assets against cyberattacks.