Network Packet Analyzer for Protocol Semantics Estimation
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Solution Overview
Problem
Conventional methods for automatic protocol reverse engineering struggle to estimate the semantics of unknown protocol fields in network packets, particularly in control systems where proprietary protocols are used, lacking scalability and requiring individual algorithm creation for each field.
Innovation Solution
A network packet analyzer that captures and filters packets, extracts variable fields, generates patterns based on parameter variations, and compares these patterns with training data to estimate the semantics of unknown protocol fields without relying on specific protocol specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional automatic protocol reverse engineering methods are used to estimate protocol field semantics, then analysis can be performed without protocol specifications, but the method lacks scalability and requires individual algorithm creation for each field
Solution Approach 1:
The patent applies universality by creating a single machine learning model that can estimate semantics for multiple different protocol fields simultaneously. Instead of requiring separate algorithms for each field type, the model learns from training data containing various field types and can generalize to estimate semantics for unknown fields, achieving multi-functionality and scalability
Solution Approach 2:
The patent utilizes parameter changes by transforming the protocol field analysis problem into a machine learning parameter estimation problem. The model takes packet data as input and outputs estimated semantic parameters (such as data type, format, and meaning) without requiring manual algorithm creation for each field, thereby changing the approach from rule-based to data-driven parameter estimation
2Measurement precision
If individual algorithms are created for each protocol field semantics, then specific field analysis can be performed, but the process becomes time-consuming and non-scalable
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on a comprehensive dataset of protocol fields before actual analysis is needed. The training phase occurs once in advance, and during subsequent analysis, the model can quickly estimate semantics for new fields without requiring time-consuming algorithm creation, thus saving time while maintaining accuracy
Solution Approach 2:
The patent uses copying by creating a virtual replica of protocol field semantics through machine learning. Instead of manually analyzing each field and creating custom algorithms, the model copies and generalizes from existing training data to automatically estimate semantics for unknown fields, reducing the time required for analysis
3Ease of operation
If protocol specifications are used for analysis, then structured protocol field definitions are available, but proprietary protocols without documentation cannot be analyzed
Solution Approach 1:
The patent applies self-service by enabling the system to analyze proprietary protocols autonomously without external documentation. The machine learning model automatically extracts patterns from raw packet data and estimates field semantics on its own, serving the analysis need without requiring human intervention to create algorithms or access to protocol specifications
Solution Approach 2:
The patent replaces the mechanical system of manual protocol analysis with an automated machine learning approach. Instead of relying on structured documentation and manual algorithm creation, the system uses data-driven learning to substitute and automatically determine protocol field semantics, enabling analysis of undocumented proprietary protocols
Data Source
AI summary
A network packet analyzer according an embodiment includes a memory and one or more hardware processors. The memory stores a plurality of sets of training data in which semantics of one protocol field and one or more patterns indicating characteristics of variations of the parameters of the one protocol field are associated with each other. The hardware processors: captures a network packets and extracts a variable field whose parameter varies in time series; generates, based on the parameter varying in the time series in the variable field, one or more patterns indicating a characteristic of a variation of the parameter; and compares each of the one or more patterns with each of the one or more patterns of the training data and estimate the semantics of the variable field.