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Real-time anomaly detection in network traffic is a method that detects unexpected and anomalous behavior by identifying normal behavior and statistical patterns in network traffic data. This method is used to detect potential attacks or other anomalous conditions in network traffic. Real-time anomaly detection uses different algorithms to detect abnormal activities in network traffic. These include statistical methods, machine learning, and deep learning techniques. By learning the normal behavior of network traffic, these methods can detect unexpected and anomalous situations. Attackers use various techniques to mimic normal patterns in network traffic, making it difficult to detect. Real-time anomaly detection allows network administrators to detect attacks faster and respond more effectively. Real-time anomaly detection can improve network performance by detecting abnormal conditions in network traffic. Abnormal traffic can overuse the network's resources and cause the network to slow down. Real-time anomaly detection detects abnormal traffic conditions, allowing network resources to be used more effectively. In this study, blockchain technology and machine learning algorithms are combined to propose a real-time prevention model that can detect anomalies in network traffic. [ABSTRACT FROM AUTHOR]