ANRAD: Adaptive Network Risk and Anomaly Detection for Risk-Based Network Breach Identification

Because network traffic is constantly growing, it can be difficult to identify unusual and possibly harmful activity. In this research, a risk-oriented system called ANRAD (Adaptive Network Risk and Anomaly Detection) is proposed. It integrates anomaly detection based on Isolation Forest, network vulnerability indicators, and traffic deviation. In order to categorize network behavior into various risk levels, these elements are combined to provide a Security Risk Score. The framework obtains an accuracy of 83.34%, precision of 77.92%, recall of 43.63%, and F1-score of 55.94% when tested on the CICIDS2017 dataset. The findings demonstrate that while attack-family analysis identifies regions in need of more development, combining several network-behavior indicators offers a cohesive strategy for network risk assessment.