Network Policy Update Recommendations for Real-Time Violation Response
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Solution Overview
Problem
Current network policy management requires intensive human intervention, leading to resource-intensive processes prone to errors and inadequate response times due to the dynamic and complex nature of modern networks.
Innovation Solution
An automated system for continuous network traffic monitoring, policy violation detection, and recommendation, utilizing machine learning and heuristic models to generate policy updates, which can be accepted or rejected by users through a graphical interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated policy adjustment is implemented, then productivity and response time are improved, but device complexity increases
Solution Approach 1:
The patent introduces a policy adjustment logic as an intermediary component that automatically generates policy updates based on network traffic analysis. This intermediary handles the complex decision-making process, isolating the complexity from the main network operations and enabling automated productivity improvement without overwhelming the core system.
Solution Approach 2:
The system segments policy management into distinct functional modules: network traffic monitoring, violation detection, policy update generation, and user interface components. This segmentation allows each module to specialize in specific tasks, improving overall productivity while managing complexity through modular design that can be developed and maintained independently.
2Reliability
If manual policy monitoring is performed, then reliability is maintained through human judgment, but loss of time increases due to manual intervention
Solution Approach 1:
The system performs preliminary actions by continuously monitoring network traffic and pre-generating policy update recommendations before violations escalate. The policy adjustment logic analyzes traffic patterns in advance and prepares corrective policies, enabling rapid response when violations occur while maintaining reliability through pre-computed accurate recommendations.
Solution Approach 2:
The system implements feedback loops where policy adjustments are continuously monitored and evaluated. The policy adjustment logic receives feedback from network traffic data and user inputs, automatically refining policy recommendations to maintain high reliability while reducing response time through iterative learning and adaptation.
3Measurement precision
If comprehensive network traffic monitoring is implemented, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system applies partial monitoring by focusing analysis on specific traffic patterns and violation indicators rather than processing all network traffic uniformly. The policy adjustment logic selectively monitors traffic that is relevant to policy compliance, achieving high measurement precision for critical violations while reducing overall energy consumption by avoiding exhaustive analysis of all network packets.
Data Source
AI summary
Devices, systems, methods, and processes for recommendation and update of network policies. Existing network policy update solutions rely on human intervention in monitoring and analyzing traffic patterns in a network, checking for policy compliance, detecting any policy violations, and even updating new policies in the network. However, manual processes are prone to human error, introduce significant delays, and lack scalability and objectivity. To address these issues, an automated system is provided that monitors traffic across a network (in real-time or near real-time) and detects violations in a set of network policies associated with the network. The system utilizes one or more recommendation models to process network flow data and network inventory data, and generate one or more policy update recommendations to resolve the detected policy violations. The system further enforces the one or more policy update recommendations on various network devices within the network to resolve the detected policy violations.