Anomaly Detection in Network Traffic Using Machine Learning for Cyber Threat Prevention

Abstract—With the growing complexity of cyber threats, anomaly detection in network traffic has become a crucial aspect of cybersecurity. This study explores the application of machine learning techniques to identify malicious activities and enhance network security frameworks. Two models—Isolation Forest, an unsupervised learning algorithm, and a Neural Network, a supervised learning approach—were employed to detect anomalies in network traffic. The Isolation Forest method leverages the rarity of anomalies, while the Neural Network captures intricate patterns in labeled data. The models were evaluated using performance metrics such as ROC-AUC, confusion matrices, and precision-recall curves. Results indicate that the Neural Network outperforms the Isolation Forest, demonstrating superior accuracy and robustness in identifying network anomalies. This research underscores the potential of machine learning-based anomaly detection in real-time cybersecurity monitoring. Future work will focus on incorporating advanced deep learning techniques and ensemble learning to further enhance detection capabilities, contributing to the development of more resilient and secure network infrastructures. Keywords—Anomaly Detection, Network Security, Machine Learning, Cyber Threat Prevention, Neural Networks