An Intelligent Machine Learning Framework for Cyber Security Threat Detection

Cyber security has become a critical concern in the digital era due to the rapid increase in cyber threats, data breaches, malware attacks, phishing attempts, and unauthorized access to information systems. Organizations generate massive amounts of network and security data that can be analyzed to identify malicious activities and prevent cyber-attacks. This study proposes an intelligent cyber security threat detection framework that integrates data preprocessing, feature selection, and machine learning classifiers for accurate threat identification. The framework utilizes network traffic and security log data to detect cyber threats in real time. Machine learning algorithms such as Support Vector Machine (SVM) and Random Forest (RF) are employed to classify normal and malicious activities. Experimental results demonstrate that the proposed approach improves detection accuracy while reducing false alarms. The framework can support intrusion detection systems, malware detection, and network security monitoring, thereby enhancing organizational cyber resilience.

Keywords:

Cyber Security, Machine Learning, Intrusion Detection System, Network Security, Threat Detection, Random Forest, Support Vector Machine, Data Analytics.

Cyber Security Machine Learning Intrusion Detection System Network Security Threat Detection Random Forest Support Vector Machine Data Analytics.