Incremental Micro-Segmentation for Dynamic ACL Policy Deployment

The system enables self-service by automatically generating ACL rules through machine learning algorithms that analyze network traffic patterns and device behaviors. The network control device autonomously learns from observed network flows and generates policy rules without requiring manual configuration by information management personnel, thereby eliminating the time-consuming manual setting process while maintaining effective network permission management.

The patent replaces the mechanical manual system with an automated intelligent system. Instead of relying on human operators to manually configure ACL rules based on their expertise and observation, the system uses machine learning algorithms and automated analysis of network traffic data to generate and update permission rules, substituting human manual operations with automated computational processes.

The system achieves consistent and reliable network permission management by eliminating dependence on individual personnel experience. The machine learning model automatically analyzes network traffic patterns and generates standardized policy rules based on learned behaviors, ensuring that the same network conditions always produce the same policy outcomes, thereby stabilizing the quality of permission management across different operators and time periods.

The system transforms the quality of network permission management from being dependent on variable human expertise to being determined by consistent algorithmic parameters. By changing the control mechanism from human judgment to automated machine learning algorithms with defined parameters and thresholds, the system achieves stable and reproducible policy generation that does not fluctuate with different personnel experiences.

The patent segments the ACL update process into independent, modular components. Instead of requiring updates to the entire ACL when adjusting network flow permissions, the system allows modification of individual policy rules or specific segments of the ACL. This segmentation enables granular adjustments to network permissions without the complexity and overhead of rewriting the complete access control list, thereby improving flexibility while reducing operational complexity.

The system introduces dynamic adaptability to the ACL mechanism by enabling real-time or near-real-time modifications to specific permission rules without requiring complete ACL reconfiguration. The firewall can dynamically adjust network flow permissions by updating only the affected policy segments, allowing the system to adapt to changing network conditions and requirements flexibly while minimizing the complexity associated with full ACL management.

The system performs preliminary action by continuously monitoring and learning network traffic patterns in advance before policy rules are needed. The machine learning model proactively analyzes network flows, identifies access patterns, and pre-generates policy rules based on learned behaviors. This preliminary analysis and rule generation eliminate the need for time-consuming manual observation of network behavior when implementing access control policies, significantly accelerating the deployment speed of security rules.

The patent implements continuous automated monitoring and analysis of network traffic, maintaining an ongoing learning process that continuously updates the machine learning model with new network behavior data. This continuous useful action replaces the intermittent manual observation process, allowing the system to maintain up-to-date knowledge of network patterns without requiring periodic manual intervention, thereby improving the speed at which access control policies can be implemented based on current network conditions.