Cloud Based Network Threat Analysis and Risk Management Using Log Analysis and Machine Learning (Random Forest)

Mrs. R. Revathi, Mr. V. Dickson Iruthayaraj, Mr.S.Sarjeeth, Mr. P. Selvakumar, Mr. S. Saran

In the era of modern technologies, introduced the widespread use of cloud computing and other solutions that revolutionized the storage and management of information. Cloud-based network threat identification and risk management using applying Log Analysis and Machine learning is intended to recognize, evaluate, and analyze cybersecurity risks in contemporary cloud settings. As cloud computing becomes more widely used, dynamic, expansive, and dispersed network infrastructures cannot be managed by conventional perimeter-based security measures. The intelligent threat detection system proposed in this research gathers and examines system and network logs produced by cloud resources in order to instantly spot malicious activity. In order to classify network behavior as either normal or abnormal, the model uses machine learning algorithms to extract pertinent data such traffic patterns, protocol usage, access frequency, and temporal behavior. In order to help security teams, prioritize incidents, risk assessment is carried out by allocating weighted scores based on threat severity, asset criticality, and previous behavior. Automated analysis, scalable log ingestion, and visual dashboards for tracking risks and threats are all supported by the architecture. By combining machine learning based random forest detection with log analysis, the suggested approach increases reaction efficiency, decreases false positives, and increases threat visibility. This project shows a realistic, affordable, and expandable solution to cloud security, which makes it appropriate for both real-world deployment scenarios and academic demonstrations. This technology has the potential to revolutionize our approach to cybersecurity and system security.