基于Ngram-TFIDF的深度恶意代码可视化分类方法

王金伟; 陈正嘉; 谢雪; 罗向阳; 马宾

doi:10.11959/j.issn.1000-436x.2024115

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基于Ngram-TFIDF的深度恶意代码可视化分类方法

学术论文 | 更新时间：2024-08-08

- 基于Ngram-TFIDF的深度恶意代码可视化分类方法
- Deep visualization classification method for malicious code based on Ngram-TFIDF
- 通信学报 2024年45卷第6期页码：160-175
- 作者机构：
  
  1.南京信息工程大学计算机学院,江苏南京 210044
  2.中国科学技术大学网络空间安全学院,安徽合肥 230031
  3.信息工程大学网络空间安全学院,河南郑州 450001
  4.齐鲁工业大学网络空间安全学院,山东济南 250353
- 作者简介：
  
  [ "王金伟（1978- ），男，内蒙古呼伦贝尔人，博士，南京信息工程大学教授，主要研究方向为多媒体版权保护、多媒体取证、多媒体加密和数据认证。" ]
  [ "陈正嘉（1999- ），男，江苏徐州人，南京信息工程大学硕士生，主要研究方向为网络安全、信息安全、恶意软件检测。" ]
  [ "谢雪（1989- ），男，吉林长春人，中国科学技术大学博士生，主要研究方向为网络安全、多媒体取证。" ]
  [ "罗向阳（1978- ），男，湖北荆门人，信息工程大学教授，主要研究方向为图像隐写和隐写分析技术。" ]
  [ "马宾（1973- ），男，山东济宁人，齐鲁工业大学教授，主要研究方向为可逆信息隐藏、多媒体取证、隐写与隐写分析。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62072250;62172435;U20B2065);中原科技创新领军人才基金资助项目;Zhongyuan Science and Technology Innovation Leading Talent Project(214200510019);江苏自然科学基金资助项目(BK20200750);河南省网络空间态势感知重点实验室开放基金资助项目(HNTS2022002);山东省计算机网络重点实验室开放课题基金资助项目(SDKLCN-2022-05)
- DOI：10.11959/j.issn.1000-436x.2024115
  中图分类号： TP309
- 收稿：2023-11-28，
  
  修回：2024-05-27，
  
  纸质出版：2024-06-25
- 稿件说明：
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王金伟,陈正嘉,谢雪等.基于Ngram-TFIDF的深度恶意代码可视化分类方法[J].通信学报,2024,45(06):160-175.

WANG Jinwei,CHEN Zhengjia,XIE Xue,et al.Deep visualization classification method for malicious code based on Ngram-TFIDF[J].Journal on Communications,2024,45(06):160-175.
王金伟,陈正嘉,谢雪等.基于Ngram-TFIDF的深度恶意代码可视化分类方法[J].通信学报,2024,45(06):160-175. DOI： 10.11959/j.issn.1000-436x.2024115.

WANG Jinwei,CHEN Zhengjia,XIE Xue,et al.Deep visualization classification method for malicious code based on Ngram-TFIDF[J].Journal on Communications,2024,45(06):160-175. DOI： 10.11959/j.issn.1000-436x.2024115.

摘要

随着恶意代码规模和种类的不断增加，传统恶意代码分析方法由于依赖于人工提取特征，变得耗时且易出错，因此不再适用。为了提高检测效率和准确性，提出了一种基于Ngram-TFIDF的深度恶意代码可视化分类方法。结合N-gram和TF-IDF技术对恶意代码数据集进行处理，并将其转化为灰度图。随后，引入CBAM并调整密集块数量，构建DenseNet88_CBAM网络模型用于灰度图分类。实验结果表明，所提方法在恶意代码家族分类和类型分类上分别提高了1.11%和9.28%的准确率，取得了优越的分类效果。

Abstract

With the continuous increase in the scale and variety of malware

traditional malware analysis methods

which relied on manual feature extraction

become time-consuming and error-prone

rendering them unsuitable. To improve detection efficiency and accuracy

a deep visualization classification method for malicious code based on Ngram-TFIDF was proposed. The malware dataset was processed by combining N-gram and TF-IDF techniques

transforming it into grayscale images. Subsequently

the CBAM was introduced and the number of dense blocks was adjusted to construct the DenseNet88_CBAM network model for grayscale image classification. Experimental results demonstrate that the proposed method achieves superior classification performance

with accuracy improvements of 1.11% and 9.28% in malware family classification and type classification

respectively.

关键词

Keywords

references

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