AI Driven Intrusion Detection Enhancing Cybersecurity with Machine Learning

Intrusion detection in cybersecurity has become crucial due to the increasing number of cyber threats and data breaches. Traditional security measures fail to detect sophisticated attacks, making machine learning (ML) an essential tool for improving security frameworks. The integration of artificial intelligence (AI) enables real-time monitoring, anomaly detection, and predictive analytics, ensuring proactive threat management. This paper discusses an AI-driven intrusion detection system (IDS) that includes supervised and unsupervised ML techniques to enhance cybersecurity resilience. The system improves accuracy, efficiency, and automation, thereby mitigating risks and reducing false positive rates. Through rigorous testing, the proposed IDS demonstrates superior performance over conventional methods.