Ai-Augmented Cybersecurity: Intelligent Threat Detection Using Machine Learning

Abstract

The traditional rule-based security frameworks are unable to handle the complex attack environment of today due dynamic implement sophisticated and flexible defenses as the adversaries' defense strategies become more complex.  To improve threat identification and response, the paper will discuss AI & ML technical results.  AI-enhanced security systems would be able to identify anomalies, predict possible vulnerabilities, and even a zero-day attack with the use of learning algorithms.  We provide a summary of used in cybersecurity, such as clustering, anomaly detection, which are forms of threat intelligence.  We also discuss if there are issues with scalability, model interpretability, adversarial attacks, and data quality in business settings. These results have demonstrated that AI-enhanced cybersecurity supports proactive, dynamic, and automated responses to changing threats in addition to enabling defending capabilities.  This study highlights how machine learning will help cybersecurity systems become more intelligent and resilient in the future.