Dnp3 Protocol Fuzzing Method Based on Sequence Generative Adversarial Network

A technology of sequence generation and fuzz testing, applied in biological neural network models, data exchange networks, neural learning methods, etc., can solve problems such as low pass rate and coverage of use cases, redundant test cases, etc. The effect of reducing redundancy and improving quality

Active Publication Date: 2021-07-30
XIAN UNIV OF SCI & TECH
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Problems solved by technology

Existing general vulnerability mining methods generally have problems such as low pass rate and coverage rate of use cases, redundant test cases, etc.

Method used

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  • Dnp3 Protocol Fuzzing Method Based on Sequence Generative Adversarial Network

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Embodiment Construction

[0037] Such as figure 1 As shown, the DNP3 protocol fuzzing method based on sequence generation confrontation network of the present invention comprises the following steps:

[0038] Step 1. Constructing the DNP3 protocol sample database: constructing the DNP3 protocol sample database using the public DNP3 protocol sample data set and a plurality of random DNP3 protocol sample data collected manually in the DNP3 industrial control system;

[0039] In this embodiment, in step 1, the random DNP3 protocol sample data is randomly captured by a packet capture tool, and the packet capture tool is a Wireshark packet capture tool.

[0040] Step 2, DNP3 protocol sample data preprocessing: Extract the DNP3 protocol sample data from the DNP3 protocol sample database according to the DNP3 protocol format and separate the DNP3 protocol sample data by field, and then preprocess the DNP3 protocol sample data through the construction method of base conversion ;

[0041] Step 3. Variation of...

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Abstract

The invention discloses a DNP3 protocol fuzzy testing method based on a sequence generation confrontation network, comprising the steps of: 1. constructing a DNP3 protocol sample database; 2. preprocessing the DNP3 protocol sample data; 3. mutating the preprocessed DNP3 protocol sample data; 4. Generate primary test cases; 5. Test case similarity comparison to remove redundancy; 6. Seed test case variation; 7. DNP3 protocol fuzz testing; 8. Vulnerability verification; 9. Abnormal test case debugging and monitoring. The invention utilizes the sequence generation confrontation network to generate test cases with a high pass rate; reduces the redundancy of test cases through a screening algorithm using the similarity of use cases as an index; introduces a random mutation strategy to mutate test cases, and improves the diversity of test cases and the probability of loophole discovery ; By analyzing the stack frame information of the program when the vulnerability occurs, find and record the cause of the vulnerability, thereby reducing the probability of the industrial control system being attacked and improving the security of the industrial control system.

Description

technical field [0001] The invention belongs to the technical field of DNP3 protocol testing and loophole mining, and in particular relates to a DNP3 protocol fuzzy testing method based on sequence generation confrontation network. Background technique [0002] Industrial control system is an automatic control system composed of computer equipment and industrial process control components. It is widely used in safety-critical fields such as electric power, water treatment, oil and gas, chemical industry, transportation, and manufacturing. As an important part of the industrial control system, the communication protocol is extremely vulnerable to attack. Therefore, how to efficiently mine potential loopholes in the protocol is crucial to improving the security of industrial control systems. [0003] There are many types of protocols for industrial control systems. Among them, the DNP3 protocol is mainly used in industrial control systems for national infrastructure such as e...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L12/26H04L29/06G06N3/04G06N3/08
CPCH04L43/50H04L63/1433G06N3/049G06N3/08G06N3/045
Inventor 于振华王浩芦刘颖王丹叶鸥赵禹博
Owner XIAN UNIV OF SCI & TECH
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