What are the benefits of using machine learning for network anomaly detection?

Machine learning enhances network anomaly detection by enabling systems to analyze vast amounts of data to identify patterns and deviations that may indicate potential threats. By training models using historical data, machine learning algorithms can detect anomalies that may be missed by traditional rule-based systems. This proactive approach helps in identifying and mitigating security threats early on, improving the overall security posture of networks. Machine learning also allows for the adaptation and updating of detection mechanisms based on real-time data, making the system more responsive to evolving threats.