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Efficient industrial control protocol analysis method based on deep learning

A technology for industrial control and protocol analysis, applied in neural learning methods, comprehensive factory control, instruments, etc., can solve problems such as inability to efficiently and accurately analyze industrial protocols, achieve high adaptability and scalability, and reduce processing time.

Pending Publication Date: 2022-05-27
SHENYANG INSTITUTE OF CHEMICAL TECHNOLOGY
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Problems solved by technology

Finally, this method of industrial control protocol analysis based on deep learning is proposed to solve the problem that most network protocol reverse tools on the market cannot efficiently and accurately analyze industrial protocols

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  • Efficient industrial control protocol analysis method based on deep learning
  • Efficient industrial control protocol analysis method based on deep learning
  • Efficient industrial control protocol analysis method based on deep learning

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Embodiment Construction

[0033] The present invention will be described in detail below with reference to the embodiments shown in the accompanying drawings.

[0034] The process of the present invention is as follows figure 1 shown, the specific steps are as follows:

[0035] S1: Acquisition of industrial protocol data packets. The data packets generated by the actual industrial control system come from the oil and gas gathering and transportation industry simulation platform in the Key Laboratory of Information Security for the Petrochemical Industry of Liaoning Province. The output is in binary form. At the same time, industrial pcap data packets for testing are collected from the github project, Wireshark wiki, etc., including some commonly used industrial control system communication protocols including S7comm, Modbus TCP, PROFIBUS, IEC104, etc.

[0036] S2: Infer functional area boundaries and types of protocol fields. The core idea is to obtain the message format by using public field informa...

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Abstract

The invention relates to an industrial control protocol analysis method, in particular to an efficient industrial control protocol analysis method based on deep learning, which comprises the following steps: acquiring flow data messages of an industrial control system from a simulation platform and an open source platform, analyzing protocol field change characteristics, and adopting an unsupervised learning method to analyze the flow data messages of the industrial control system. A voting expert algorithm (VE) performs sequence segmentation and format feature inference on protocol fields. And taking the processed field sequence features as input, building a bidirectional long-short-term memory neural network model (BiLSTM-AM) added with an attention mechanism, performing training, using softmax as a protocol field classifier, and realizing industrial protocol field classification result prediction according to a classification result. According to the invention, based on the bidirectional long-short-term memory neural network model added with the attention mechanism, a good detection result is achieved in unknown industrial protocol prediction and classification; the problem that most network protocol reverse tools in the current market cannot efficiently and accurately analyze the industrial protocol is solved.

Description

technical field [0001] The invention relates to an industrial control analysis method, in particular to an efficient industrial control protocol analysis method based on deep learning. Background technique [0002] Industrial control systems are an important part of many critical infrastructures and have a major impact on their security. With the rapid development of industrial control systems, more and more computer networks are used in industrial control systems, which brings many security issues to industrial control systems. Protocol security is one of the most important security issues. [0003] Many industrial protocols are unknown, which makes it impossible for firewalls to parse and analyze network traffic, which brings great challenges to intrusion detection, deep packet inspection and traffic management. The detection of abnormal communication behavior of industrial protocols in existing industrial control systems basically relies on the deep analysis features of ...

Claims

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Application Information

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IPC IPC(8): H04L69/22G06K9/62G06N3/04G06N3/08
CPCH04L69/22G06N3/088G06N3/044G06N3/045G06F18/241Y02P90/02
Inventor 宁博伟宗学军何戡郑洪宇杨忠君连莲孙逸菲
Owner SHENYANG INSTITUTE OF CHEMICAL TECHNOLOGY
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