Analyzing and Predicting Cyber Threats Using Machine Learning Algorithms

As cyberattacks grow in volume and sophistication, traditional security systems prove insufficient. This study applies Random Forest and XGBoost ensemble algorithms to the CICIDS2017 dataset for network traffic anomaly detection. XGBoost achieved 99.2% accuracy and an F1-Score of 0.991, providing a practical foundation for proactive cybersecurity systems.

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