A Machine Learning-based Framework for Monitoring and Mitigating Unauthorized Access in Computer Networks using Apache Web Logs

Security of dynamic networks depends on access control, yet this security approach struggles to meet modern IoT and M2M demands. The proposed framework unifies Machine Learning anomaly detection with Blockchain decentralized authentication and Zero Trust security models. Dynamic permission management together with real-time threat detection produces a framework that delivers reliable, adaptable, and scalable access controls. Security operations within industries including smart cities, healthcare, and industrial automation benefit from the system’s capabilities.