The technology used for detecting anomalies in network traffic is often based on intrusion detection systems (IDS) and machine learning algorithms. IDS can analyze real-time data to identify patterns that deviate from normal behavior, signaling potential security threats. Additionally, machine learning enhances these systems by learning from historical traffic patterns and continuously adapting detection techniques. By recognizing unusual activities, IDS and machine learning combine to provide organizations with early warnings of potential breaches, allowing them to take proactive measures to protect their networks and sensitive data from cyber threats.