In the current digital landscape, network security is a crucial concern due to the growing complexity and frequency of cyber threats. Anomaly detection in network traffic plays a vital role in identifying malicious activities and preventing potential intrusions. This project explores the implementation of advanced machine learning techniques, specifically the Random Forest and Decision Tree classifiers, to detect anomalies in network traffic data. The study addresses key challenges such as class imbalance, high-dimensional feature space, and dynamic traffic patterns. By evaluating these models on standard datasets, we analyze their performance in terms of accuracy, precision, recall, and F1-score. The results demonstrate the efficacy of these models in accurately identifying abnormal patterns while maintaining a low false positive rate. The proposed approach contributes to enhancing network security by enabling timely and reliable detection of anomalous activities.

Keywords: Anomaly Detection, Network Traffic, Cybersecurity, Machine Learning, Random Forest, Decision Tree, Class Imbalance, Feature Complexity, Intrusion Detection, Network Security.