Network Traffic Anomaly Detection

Network traffic anomaly detection aims to identify malicious activities or system failures within network data by distinguishing normal from abnormal patterns. Current research emphasizes improving detection accuracy through advanced techniques like multi-view feature fusion, which combines insights from different data perspectives, and semi-supervised learning methods that leverage only normal traffic data for model training. Graph neural networks and other deep learning architectures, such as stacked autoencoders, are increasingly used to capture complex temporal and contextual relationships within network traffic, leading to more robust and accurate anomaly detection. These advancements are crucial for enhancing cybersecurity and ensuring the reliable operation of computer networks.