[Reference: NIST SP 800-94, Guide to Intrusion Detection and Prevention Systems (IDPS)., Real-Time Detection and Mitigation:, By leveraging machine learning, the system can continuously learn and adapt to new traffic patterns, reducing false positives and ensuring accurate real-time threat detection and mitigation., Reference: IEEE Transactions on Network and Service Management., Reduction of False Positives:, A machine learning-based approach can distinguish between benign anomalies and actual threats by considering context, historical data, and behavioral patterns, thereby minimizing false positives., Reference: SANS Institute’s Intrusion Detection FAQ., Handling Evolving Threats:, The dynamic nature of machine learning allows the baseline to evolve as new types of traffic and threats emerge, ensuring that the security system remains effective against both known and unknown threats., Reference: ENISA’s Guidelines for Network Security Monitoring., Using machine learning to establish a dynamic baseline is an effective strategy for NetSafe Corp to maintain robust network security and respond to threats promptly., , ]