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A Malicious Code Detection Method Inspired by Biological Genes

A malicious code detection and malicious code technology, applied in the fields of instruments, computing, electrical digital data processing, etc., can solve the problem of difficult to ensure the security detection of malicious code, achieve rapid security detection, reduce analysis scale, and ensure security detection. Effect

Active Publication Date: 2020-09-29
PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

[0006] The present invention aims at the problem that most of the malicious code analysis methods based on genetic inspiration have their own limitations, so that it is difficult to ensure the effective security detection of malicious code, and proposes a method inspired by biological genes. Malicious code detection method

Method used

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  • A Malicious Code Detection Method Inspired by Biological Genes
  • A Malicious Code Detection Method Inspired by Biological Genes
  • A Malicious Code Detection Method Inspired by Biological Genes

Examples

Experimental program
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Effect test

Embodiment 1

[0041] like figure 1 As shown, a malicious code detection method inspired by biological genes includes the following steps:

[0042] Step S101: defining a malicious code software gene, the malicious code software gene is an instruction sequence, and the end instruction of the instruction sequence is one of return, jump, switch or call;

[0043] Step S102: performing malware gene extraction on the code based on the defined malware gene;

[0044] Step S103: Obtain the distance value between the extracted malicious code software genes through the Smith-Waterman algorithm;

[0045] Step S104: clustering the extracted malware genes according to the distance value between the extracted malware genes;

[0046] Step S105: according to the clustering result, the malware gene is mapped to the feature vector, and each type of malware gene corresponds to the one-dimensional data of the feature vector;

[0047] Step S106: According to the feature vector, a malicious code detector is con...

Embodiment 2

[0066] like figure 2 As shown, another malicious code detection method inspired by biological genes includes:

[0067] Step S201: defining malicious code software genes;

[0068] Biological genes exist in the form of a continuous deoxyribonucleic acid sequence. When the gene is expressed, the entire sequence is transcribed and translated. When a gene is an intron, the entire sequence is not expressed. Correspondingly, in order to describe software code fragments easily using sequences, this system regards a code sequence that executes consistently as a malicious code software gene.

[0069] Definition of malicious code software gene: malicious code software gene is an instruction sequence, and the end instruction of the instruction sequence is one of return, jump, switch or call. In fact, a gene is composed of one or more basic blocks. The basic block takes all call jump instructions as segmentation boundaries. This definition is too trivial for malicious code analysis. ...

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Abstract

The invention relates to the technical field of malicious code detection, and discloses a malicious code detection method inspired by biological genes, including: defining a malicious code software gene; performing gene extraction on the code based on the defined malicious code software gene; The distance value between the extracted malicious code software genes; the extracted malicious code software genes are clustered according to the extracted malicious code software gene distance values; according to the clustering results, the malicious code software genes are mapped to feature vectors, each class The one-dimensional data of the feature vector corresponding to the malicious code software gene; according to the feature vector, a malicious code detector is constructed through a machine learning model, and the malicious code in the code to be tested is detected by the malicious code detector. The malicious code detector generated by the invention has a higher detection accuracy rate of malicious codes.

Description

technical field [0001] The invention relates to the technical field of malicious code detection, in particular to a method for detecting malicious code inspired by biological genes. Background technique [0002] In recent years, malware such as Mirai, WannaCry, and BlackEnergy have emerged one after another, causing huge losses to the world. According to Tencent's "Internet Security Report 2017", approximately 136 million new malware samples were detected in 2017 (Tencent United Security Laboratory. https: / / slab.qq.com / news / authority / 1708.html(2018) ). Millions of new malware samples are produced every year with the help of automated tools. Traditional methods, such as signature matching or rule-based detection, lack the ability to detect unknown malware and its variants. Manual analysis is accurate but inefficient. Therefore, it has become an inevitable trend in this field to rely on machine learning algorithms to learn from the massive data generated by automated analy...

Claims

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Application Information

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IPC IPC(8): G06F21/56G06K9/62
CPCG06F21/563G06F18/23G06F18/2411
Inventor 刘福东单征林成梁陈奕杭侯一凡李星玮桂海仁孙文杰
Owner PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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