Secure Data Transmission via ML-Generated Filtering Rules
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Solution Overview
Problem
Existing secure data transmission systems require manual creation of filtering rules, which can be inefficient and may not adapt effectively to customized or dynamic security needs.
Innovation Solution
A system utilizing a programmable logic device and a server with a machine learning model to automatically generate filtering rules by predicting protocol and command types of data packets, selecting relevant feature values, and deploying these rules for secure data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual filtering rules are created by engineers, then customization and knowledge-based security are improved, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically analyzing data packets and generating filtering rules in advance, so that when security threats arise, pre-established rules are already in place to protect the system without requiring manual intervention at the moment of threat detection
Solution Approach 2:
The filtering device performs self-service by automatically generating its own filtering rules through the rule generation module, which analyzes packet data and creates security rules without external human intervention, thereby maintaining high security reliability while eliminating time consumption associated with manual rule creation
2Measurement precision
If manual filtering rules are created by engineers, then rule accuracy and security knowledge application are improved, but productivity and automation level deteriorate
Solution Approach 1:
The system replaces the mechanical process of manual rule creation with an automated information processing system. The rule generation module uses algorithmic analysis of packet data to generate filtering rules, substituting human engineering work with automated computational processes that maintain accuracy while dramatically increasing productivity
Solution Approach 2:
The rule generation module serves as an intermediary between raw packet data and filtering rules. It processes packet information, extracts security-relevant features, and generates accurate filtering rules automatically, bridging the gap between data observation and security policy implementation without requiring direct human involvement
3Adaptability or versatility
If manual filtering rules are created, then adaptability to specific security needs is improved, but device complexity and operational burden increase
Solution Approach 1:
The system implements dynamics by making the filtering rule generation process adaptive and responsive to changing security conditions. The rule generation module continuously analyzes packet data and automatically adjusts filtering rules based on observed patterns, allowing the system to adapt to new security threats without increasing operational complexity for users
Data Source
AI summary
A method for secure data transmission and a system using the same method are provided. The method includes: coupling a programmable logic device to a server; receiving a first data packet by the server; inputting the first data packet into a machine learning model to predict a protocol type and a command type of the first data packet by the server; verifying whether the protocol type and the command type are correct by the server; adding the first data packet to a data packet information set in response to the protocol type and the command type being correct by the server; generating a filtering rule according to the data packet information set and deploying the filtering rule in the programmable logic device by the server; and performing the secure data transmission according to the filtering rule by the programmable logic device.