Automatic Filter Rule Generation for Aviation Network Security
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Solution Overview
Problem
Current cybersecurity measures for aviation platforms and infrastructures are inadequate due to the complexity of networked systems and the lack of automated generation of machine-understandable filter rules, leading to errors and vulnerabilities in preventing unauthorized data flows.
Innovation Solution
A method and system for automatically generating filter rules by capturing and parsing network packets to extract attributes, classify functional network flows, and create IPtables-like rules, including deep packet filtering, to control data flows and prevent unauthorized access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation is used to create network filter rules, then human judgment can be applied, but errors and coverage gaps occur
Solution Approach 1:
The system performs self-service by automatically analyzing network traffic patterns and generating filter rules without human intervention. The automated system extracts attributes from packets, identifies functional network flows, and produces IPtables-like rules autonomously, eliminating manual interpretation errors while maintaining high accuracy through machine learning and pattern recognition algorithms.
Solution Approach 2:
The patent replaces the mechanical manual process of interpreting network definitions and creating filter rules with an automated computational system. The system uses packet capture, parsing, and attribute extraction mechanisms to substitute human operators, achieving both high automation and precision through algorithmic analysis of network traffic patterns.
2Productivity
If automated generation of filter rules is implemented, then productivity increases, but complexity of the system increases
Solution Approach 1:
The automated system is segmented into distinct functional modules: packet capture module, parsing module, attribute extraction module, functional network flow identification module, and filter rule generation module. Each module handles a specific task, reducing overall system complexity while maintaining high productivity through parallel processing and specialized optimization of each component.
Solution Approach 2:
The system employs universal components that perform multiple functions - the packet parser extracts various attributes (source/destination addresses, ports, protocols) simultaneously, the functional network flow identification handles both classification and pattern recognition, and the rule generation engine produces multiple types of filter rules from the same analysis, reducing the need for separate specialized systems.
3Reliability
If deep packet filtering rules are created to prevent unauthorized data flows, then security is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-processing and analyzing network packets during capture, extracting all relevant attributes and identifying functional network flows before filter rules are applied. This upfront analysis creates a ready-to-use profile that speeds up subsequent filtering operations, maintaining high security through comprehensive analysis while reducing processing time during actual packet filtering.
Solution Approach 2:
The system implements dynamic filtering by adapting filter rule application based on identified functional network flows and traffic patterns. The system dynamically adjusts which deep packet filtering rules are applied to different traffic types, optimizing processing time by avoiding unnecessary deep inspection of already-validated traffic while maintaining rigorous security checks on suspicious or unauthorized flows.
Data Source
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AI summary
In general, certain examples of the present disclosure provide methods and systems for automatic generation of filter rules based on functional network flows for e-Enabled aviation systems. According to various examples, a method is provided comprising capturing network packets corresponding to a functional network flow transmitted within a networked aviation system, and parsing the network packets in order to extract one or more network messages corresponding to the functional network flow. The network message is examined in order to identify and classify a plurality of attributes corresponding to the header and data fields of the network packets. A table corresponding to the network messages is automatically generated, which includes one or more filter rules. In some examples, the table may be used to determine which communications are authorized during a particular context of the networked aviation system. The method further comprises validating the one or more filter rules.