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Embedded terminal software code vulnerability detection method and device based on neural network

An embedded terminal and neural network technology is applied in the field of embedded terminal software code vulnerability detection. Detection efficiency, the effect of overcoming dependencies

Pending Publication Date: 2020-08-18
CHINA ELECTRIC POWER RES INST +3
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to provide a neural network-based embedded terminal software code vulnerability detection method and device, to alleviate the problems in the prior art that cannot correctly identify character strings or constants, which are caused by excessive reliance on graph matching algorithms. Technical problems with inaccurate judgment results and low operating efficiency

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  • Embedded terminal software code vulnerability detection method and device based on neural network
  • Embedded terminal software code vulnerability detection method and device based on neural network
  • Embedded terminal software code vulnerability detection method and device based on neural network

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Embodiment 1

[0037] According to an embodiment of the present invention, an embodiment of a neural network-based embedded terminal software code vulnerability detection method is provided. It should be noted that the steps shown in the flow chart of the accompanying drawings can be implemented in such as a set of computer-executable instructions and, although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that shown or described herein.

[0038] figure 2 It is a flow chart of a neural network-based embedded terminal software code vulnerability detection method according to an embodiment of the present invention, such as figure 2 As shown, the method includes the following steps:

[0039] In step S101, the source code of the target embedded terminal software is obtained, and the source code is preprocessed to obtain a binary code.

[0040] In the embodiment of the present invention, because the binary co...

Embodiment 2

[0099] The embodiment of the present invention also provides a neural network-based embedded terminal software code vulnerability detection device, the neural network-based embedded terminal software code vulnerability detection device is mainly used to implement the neural network-based The network embedded terminal software code vulnerability detection method, the following is a specific introduction to the neural network-based embedded terminal software code vulnerability detection device provided by the embodiment of the present invention.

[0100] Figure 10 It is a schematic structural diagram of a neural network-based embedded terminal software code vulnerability detection device provided by an embodiment of the present invention. Such as Figure 10 As shown, this neural network-based embedded terminal software code vulnerability detection device mainly includes: obtaining a preprocessing module 11, a first input module 12, a second input module 13 and a determination ...

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Abstract

The invention provides an embedded terminal software code vulnerability detection method and device based on a neural network, and the method comprises the steps: obtaining a source code of target embedded terminal software, and carrying out the preprocessing of the source code to obtain a binary code; inputting the feature function of the binary code into a pre-trained neural network to obtain anattribute control flow graph of the binary code; inputting the attribute control flow diagram of the binary code and the attribute control flow diagram of the vulnerability code into a Siamese network for similarity comparison to obtain a comparison result; wherein the vulnerability code is a known vulnerability in a preset vulnerability library; and finally, determining whether the source code of the embedded terminal software has a known vulnerability or not based on a comparison result. According to the method, the universality of detection is improved by converting the source code into the binary code through preprocessing, meanwhile, the limitation of a graph matching algorithm is overcome by generating the attribute control flow graph based on the neural network, and the detection efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of smart grid security, in particular to a neural network-based embedded terminal software code vulnerability detection method and device. Background technique [0002] Embedded technology plays a vital role in power systems, and embedded devices (ie, embedded terminals) used in power systems have the characteristics of various types and diversified structures. While these embedded devices provide various functions, they are also prone to security risks. Studies have shown that more than 80.4% of embedded device firmware contains multiple N-day vulnerabilities when the vendor releases them, and even contains vulnerabilities that have been disclosed for more than 8 years. Because the embedded device code has the same source and slow update, a source code level vulnerability may spread to hundreds of other embedded devices with different hardware architectures and software platforms, and may exist in these de...

Claims

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Application Information

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IPC IPC(8): G06F21/56G06F21/57G06N3/08
CPCG06F21/563G06F21/577G06N3/08
Inventor 朱朝阳颜秉晶周亮王海翔冀晓宇徐文渊应欢张燕秒卢新岱韩丽芳缪思薇朱亚运李霁远
Owner CHINA ELECTRIC POWER RES INST
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