Network Security Policy Generation via Machine Learning
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Solution Overview
Problem
Existing host-based network security technologies, such as firewalls, require tedious and error-prone manual processes for generating, modifying, and deleting security policies, especially during changes in network topology or device connections, which can be time-consuming and prone to errors.
Innovation Solution
The use of machine learning to generate network communication policies based on existing network communications, without the need for labeled training data, allowing for the validation of communications between applications over a network by creating human-readable rules that balance permissiveness and restrictiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to generate, modify, and delete security policies, then policy accuracy can be controlled, but the process becomes tedious, time-consuming, and error-prone
Solution Approach 1:
The system automatically generates security policies by analyzing network traffic patterns and application behaviors without requiring manual intervention. The machine learning model self-adjusts and updates policies based on observed network conditions, eliminating the need for manual policy creation while maintaining accuracy through data-driven decision-making
Solution Approach 2:
Manual mechanical processes of policy creation are replaced with automated machine learning algorithms that process network data and generate policies computationally. The system substitutes human operators with AI models that can rapidly analyze network traffic and produce accurate security policies without time consumption
2Reliability
If manual processes are used to generate, modify, and delete security policies, then policy accuracy can be controlled, but the process becomes prone to errors
Solution Approach 1:
The system automatically generates security policies by analyzing network traffic patterns and application behaviors without requiring manual intervention. The machine learning model self-adjusts and updates policies based on observed network conditions, eliminating the need for manual policy creation while maintaining accuracy through data-driven decision-making
Solution Approach 2:
Manual mechanical processes of policy creation are replaced with automated machine learning algorithms that process network data and generate policies computationally. The system substitutes human operators with AI models that can rapidly analyze network traffic and produce accurate security policies without time consumption
3Reliability
If existing host-based firewalls are used to monitor connections, then directional access control is achieved, but the system complexity increases during network topology changes
Solution Approach 1:
The machine learning model serves multiple functions: it analyzes network traffic, identifies application behaviors, generates security policies, and adapts to topology changes simultaneously. This multi-functional approach consolidates what would otherwise require multiple separate systems into a single unified platform, reducing overall system complexity
Solution Approach 2:
The system dynamically adapts to changing network topologies and application behaviors through continuous machine learning. Rather than requiring manual reconfiguration when network conditions change, the model automatically updates its understanding of legitimate traffic patterns and adjusts policies accordingly, reducing operational complexity
Data Source
AI summary
Embodiments of the present invention generate network communication policies by applying machine learning to existing network communications, and without using information that labels such communications as healthy or unhealthy. The resulting policies may be used to validate communication between applications (or services) over a network.