Enhancing Cyber Defense: Using Machine Learning Algorithms for Detection of Network Anomalies

Developing advanced cyber defense techniques is essential for effectively detecting network anomalies that are becoming more challenging to identify. In this paper, we generate machine learning models based on real-time Internet and historical data and evaluate their classification performance. We introduce a network anomaly detection tool CyberDefense that integrates various stages of the anomaly detection process. It facilitates performance evaluation of machine learning algorithms and generation of new machine learning models. Its modular and scalable design enables incorporating new datasets and machine learning algorithms. The tool has been utilized to generate models and evaluate their classification performance using datasets collected during reported power outage and ransomware attacks.