NETWORK TRAFFIC ANALYSIS USING PYTHON FOR ANOMALY DETECTION

Abstract

The rapid growth of digital communication has made network security a critical concern for organizations. Detecting anomalies in network traffic is essential to identify intrusions, cyberattacks, and abnormal user behaviors in real time. This study presents an approach for network traffic analysis using Python-based machine learning techniques to accurately detect anomalies. The proposed framework involves data preprocessing, feature extraction, and model training on benchmark datasets such as CICIDS2017 and UNSW-NB15. Various supervised and unsupervised algorithms, including Isolation Forest, Random Forest, and Gradient Boosting, are implemented and compared based on accuracy, precision, recall, and F1-score. The system is designed to automate the detection process and minimize false alarms while maintaining high detection accuracy. Experimental results demonstrate that Python’s open-source ecosystem provides an efficient and scalable environment for developing anomaly detection systems suitable for modern network infrastructures. The proposed solution enhances network monitoring and contributes to building intelligent intrusion detection mechanisms.