Smart SDN Pod Behavior Modeling for Dynamic Intrusion Blocking
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Solution Overview
Problem
Existing network security solutions struggle to dynamically adapt to the behaviors of nodes in a software-defined network, making it difficult to effectively segment and protect enterprise computer networks from intrusions, especially in cloud environments where physical links are absent.
Innovation Solution
A system that records and clusters pod behaviors to generate a behavior transition model, using AI to detect misbehaviors and trigger network policy changes, creating pod-specific dynamic firewalls to block suspicious communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If micro segmentations with network policies are implemented to protect enterprise networks from intrusions, then network security is improved, but the complexity of network administration increases as administrators must understand the behaviors of each node to adequately define policies
Solution Approach 1:
The system enables self-service by automatically generating network policies through AI/ML analysis of node behaviors. The system autonomously monitors, analyzes, and creates segmentation policies without requiring administrators to manually understand complex node behaviors, thus maintaining high security while reducing administrative burden
Solution Approach 2:
The system changes the parameter of policy generation from manual configuration to automated AI/ML-driven generation. By transforming the approach from human-based policy definition to machine-based automatic policy creation, the system resolves the contradiction between security requirements and administrative complexity
2Ease of manufacture
If static network policies are used to segment the network, then implementation is simpler, but the system cannot dynamically adapt to changing behaviors of nodes in the network
Solution Approach 1:
The system applies dynamics by implementing continuous monitoring and real-time analysis of node behaviors using AI/ML algorithms. Network policies are dynamically updated based on observed behavior changes, enabling the system to adapt to evolving threats and patterns while maintaining automated simplification of policy management
Solution Approach 2:
The system incorporates feedback loops where node behaviors are continuously monitored, analyzed by AI/ML models, and used to automatically adjust network policies. This closed-loop feedback mechanism enables dynamic adaptation while keeping the implementation process automated and simple
3Measurement precision
If comprehensive monitoring of all node behaviors is implemented to detect intrusions, then detection accuracy is improved, but the computational resources and time required for analysis increase
Solution Approach 1:
The system applies partial action by focusing AI/ML analysis on critical behavior patterns and anomalies rather than processing all node activities uniformly. The system identifies and prioritizes significant behavioral deviations that indicate potential intrusions, achieving high detection accuracy while reducing overall analysis time through selective focus on the most informative signals
Data Source
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AI summary
A method, computer system, and a computer program product for smart SDN is provided. The present invention may include recording and clustering a pod's behavior to generate a behavior transition model for the pod. The present invention may include watching a behavior of the pod and comparing the behavior to the generated behavior transition model. The present invention may include triggering a network policy change based on determining that the behavior of the pod is a misbehavior.