System for predicting industrial control network vulnerability based on N-gram

A loophole, industrial control technology, applied in the computer field, can solve problems such as the inability to guarantee the security of industrial control networks, and achieve the effects of improving accuracy and prediction efficiency, fast computing speed, and improving accuracy and training efficiency.

Active Publication Date: 2022-02-08
山东云天安全技术有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, if the outbreak of industrial control network vulnerabilities cannot be predicted in time and...

Method used

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  • System for predicting industrial control network vulnerability based on N-gram
  • System for predicting industrial control network vulnerability based on N-gram
  • System for predicting industrial control network vulnerability based on N-gram

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0035] The computer program stored in the storage medium includes a first computer program, and when the processor executes the first computer program, the following steps are implemented:

[0036] Step S101, obtain the training period T of the training data set 0 =LCM(TP m ), LCM is the least common multiple function.

[0037] Due to different P m and Q n The update period of t is very different. If the sliding window is directly used to select the training parameters, many parameters will not change within a certain period of time, which wastes computing resources and has little significance for model training. Therefore, this embodiment selects TP m The small common multiple of is used as the training period. It should be noted that since the update period of the characteristic parameters of Internet vulnerabilities is much longer than that of the characteristic parameters of industrial control network, only the update period of characteristic parameters of Internet vul...

Embodiment 2

[0063] It should be noted that there are many characteristic parameters of Internet vulnerabilities and are easy to obtain. However, in some application scenarios, due to various factors such as the scale of the industrial control network, it may not be possible to obtain sufficient characteristic parameters of the vulnerability of the industrial control network to train the industrial control network. Vulnerability Prediction Model. However, since the trend of the outbreak of the same vulnerability in the industrial control network is consistent with the overall trend of the Internet outbreak, there is correlation. Therefore, the outbreak probability of the industrial control network vulnerability can be calculated based on the correlation between the industrial control network and the Internet vulnerability outbreak and the characteristic parameters of the Internet vulnerability. predict.

[0064] Specifically, the computer program stored in the storage medium includes a sec...

Embodiment 3

[0093] The computer program stored in the storage medium includes a third computer program, and when the processor executes the third computer program, the following steps are implemented:

[0094] Step S300, obtain the text sequence {Str of each sample vulnerability id in the corresponding Summary from the database 1 ,Str 2 ,...},Str e It is the summary text corresponding to the e-th update period, and the value range of e is from 1 to infinity.

[0095] Step S301, when e=1, according to Str e The length of Str e The feature weight w e .

[0096] Through step S301, for each Str e Set the corresponding initial feature weights.

[0097] Step S302, when e>1, compare Str e-1 and Str e If the text information of is completely consistent, then judge z*w e-1 Whether it is greater than the preset first feature weight threshold w emin , if greater than, then set w e =z*w e-1 , where z is the preset weight adjustment coefficient, 0e-1 less than or equal to w emin , then s...

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Abstract

The invention relates to a system for predicting industrial control network vulnerabilities based on N-gram, which is implemented by the following steps: S601, removing industrial Internet stop words in Sore based on an industrial Internet stop word bank, and segmenting the Sore at the positions of the industrial Internet stop words to generate corresponding text fragment sequences; step S602, performing preset N-gram word segmentation processing on each Frei, and performing combination and duplicate removal on segmented words of all the Frei of each Sret to obtain a corresponding segmented word vector FBe; step S603, combining and de-duplicating the segmented words in all the FBe to obtain a segmented word set FC, and determining the number of the segmented words of the FC as the dimension of one-hot coding; step S604, performing one-hot coding on the word segmentation vector FBe based on the dimension of one-hot coding, and obtaining an original feature parameter value of each Sret; and step S605, training based on the original characteristic parameter value of Sre to obtain an industrial control network vulnerability prediction model, and predicting the vulnerability outbreak probability of the industrial control network. According to the invention, the vulnerability outbreak probability of the industrial control network can be quickly and accurately predicted, and the safety of the industrial control network is improved.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a system for predicting loopholes in industrial control networks based on N-grams. Background technique [0002] With the accelerated integration of cloud computing, big data, artificial intelligence, Internet of Things and other new-generation information technologies and manufacturing technologies, industrial control systems have changed from closed and independent to open, from stand-alone to interconnected, and from automation to intelligence. While industrial enterprises have gained great momentum for development, a large number of security risks have also emerged, from the Stuxnet virus targeting the Iranian nuclear plant in 2010 to the Havex virus sweeping Europe in 2014, etc., targeting industrial control system networks (hereinafter referred to as industrial control networks) Attacks are becoming more and more serious, and industrial control systems are in urgent need ...

Claims

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Application Information

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IPC IPC(8): G06F21/57
CPCG06F21/577
Inventor 李峰李艳虎程学志姜明时伟强张洪铭
Owner 山东云天安全技术有限公司
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