Network traffic analysis plays a crucial role in cybersecurity by detecting and mitigating threats to network infrastructure. Anomaly detection is a fundamental technique used in network traffic analysis to identify unusual patterns or behaviors that deviate from normal traffic. This paper provides an overview of anomaly detection approaches in network traffic analysis, focusing on their principles, methodologies, and applications. We discuss traditional methods such as statistical analysis and machine learning, as well as recent advancements including deep learning and ensemble techniques. The paper also highlights challenges and future research directions in the field of anomaly detection for network traffic analysis.