Leveraging AI for Proactive Threat Detection: A Machine Learning Approach to Cybersecurity

Abstract

Cyber threats in today's fast-paced digital world are getting smarter, therefore security solutions need to be more flexible and responsive. It is necessary to create proactive protection mechanisms in cybersecurity because traditional reactive methods frequently fail to identify new threats in a timely manner. improving cybersecurity through the use of AI and ML, with an emphasis on proactive threat detection. Machine learning algorithms are able to anticipate and prevent security breaches by examining large datasets for unusual patterns, behaviors, and abnormalities. The study delves into different AI-driven methods, like automated threat hunting, anomaly detection, and predictive analytics, and shows how well they can detect advanced threats like ransomware and zero-day attacks. Also included is a review of the various machine learning algorithms used in cybersecurity applications, including decision trees, support vector machines, neural networks, and others, and how well they scale, respond, and accurately identify threats. data privacy, model openness, and the possibility of adversarial machine learning assaults are some of the ethical concerns and problems with using AI in cybersecurity. We hope to show that AI-powered solutions can make cybersecurity more proactive rather than reactive, which will strengthen digital security as a whole.