INTELLIGENT THREAT DETECTION

The increasing dependence on digital communication networks and cloud-based infrastructures has significantly expanded the attack surface for cyber threats. Traditional security mechanisms that rely on predefined rules and signature-based detection often fail to identify sophisticated or previously unknown attacks. To address these limitations, intelligent threat detection using Machine Learning (ML) has emerged as an effective approach for enhancing modern network security systems. Machine learning techniques enable automated analysis of large volumes of network traffic data to identify abnormal patterns, suspicious activities, and potential intrusions in real time.

This study focuses on the application of machine learning algorithms for detecting cyber threats through behavioral analysis and anomaly detection. By utilizing supervised and unsupervised learning models, intelligent systems can differentiate between normal and malicious network activities with improved accuracy. The proposed approach emphasizes data preprocessing, feature extraction, model training, and continuous monitoring to enhance detection efficiency while reducing false positives. Additionally, automated response mechanisms support faster mitigation of security incidents