Machine Learning Air Gapping for Real-Time Network Port Isolation
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Solution Overview
Problem
Existing network security systems struggle to effectively identify and isolate network ports under threat in real-time, leading to potential security breaches and network disruptions.
Innovation Solution
A machine learning (ML) based system that monitors data traffic across network ports, identifies security threats through trained ML models, and isolates potentially compromised ports, rerouting traffic to redundant ports to maintain network integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network security systems are used to monitor and identify security threats, then the system structure is simple and easy to implement, but the threat detection accuracy is low and real-time identification capability is insufficient
Solution Approach 1:
The patent introduces a machine learning subsystem as an intermediary component between the data traffic monitoring module and the threat identification process. This ML subsystem processes data traffic patterns and provides enhanced threat detection capabilities, thereby improving measurement precision without requiring complete redesign of the entire network security system.
Solution Approach 2:
The patent replaces traditional rule-based or signature-based threat detection mechanisms with machine learning-based pattern recognition. The ML model analyzes data traffic patterns to identify security threats, substituting mechanical or algorithmic rule-based systems with adaptive learning-based systems that achieve higher detection accuracy.
2Reliability
If network ports are isolated to prevent security threat propagation, then security reliability is improved, but network connectivity is disrupted and normal data traffic is blocked
Solution Approach 1:
The patent implements dynamic air gapping where network ports are isolated only when security threats are detected through ML analysis. The system continuously monitors data traffic patterns and dynamically adjusts the connectivity state of network ports, maintaining normal operation during safe conditions and providing rapid isolation when threats are identified, thus balancing security reliability with network productivity.
Solution Approach 2:
The system employs feedback mechanisms where the ML subsystem continuously analyzes data traffic patterns and provides feedback to control whether air gapping should be applied. This feedback loop ensures that network ports are isolated only when necessary for security, preventing unnecessary disruptions to normal network operations while maintaining security reliability.
3Measurement precision
If machine learning models are deployed for real-time threat detection, then threat identification accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies machine learning models selectively to data traffic patterns that exhibit suspicious characteristics or meet certain thresholds. Rather than analyzing every single data packet in real-time, the system uses the ML subsystem to process only relevant patterns, thereby reducing processing time and computational resource requirements while maintaining high threat identification accuracy.
Data Source
AI summary
Systems, computer program products, and methods are described herein for machine learning (ML) based network resilience and steering. An example system monitors data traffic across one or more network ports and determines a first data traffic pattern from the data traffic. The system further determines, via a ML subsystem, that the first data traffic pattern is indicative of a security threat to a first network port. In response to determining that the first data traffic pattern is indicative of the security threat to the first network port, the system further isolates the first network port from the one or more network ports.