A Machine Learning-Based Framework for Network Traffic Anomaly Detection Using Isolation Forest

The exponential growth in network traffic volume, velocity, and heterogeneity has rendered manual threat monitoring operationally infeasible for modern enterprise, cloud, and hybrid infrastructures. Conventional signature-based intrusion detection systems (IDS) perform reliably for catalogued attack fingerprints but exhibit critical detection gaps when confronted with polymorphic, obfuscated, or zero-day attack behaviors that have no prior signature representation. This paper presents an anomaly-driven intrusion detection framework grounded in Isolation Forest — an unsupervised machine learning algorithm that isolates statistically rare patterns in high-dimensional data without requiring labeled attack samples during training. The proposed system integrates data ingestion, feature preprocessing, model training, anomaly scoring, quantitative evaluation, and dashboard-based visual reporting into a single reproducible end-to-end pipeline. Validation on an NSL-KDD based benchmark dataset comprising 22,543 records and 42 attributes yields Accuracy = 0.730, Precision = 0.799, Recall = 0.702, and F1-score = 0.747 under label-aware evaluation. These results confirm that a computationally lightweight unsupervised model can serve as an effective first-stage network threat detector while preserving operational interpretability and deployment feasibility.