Review of malware detection and classification visualization techniques

Jinwei WANG; Zhengjia CHEN; Xue XIE; Xiangyang LUO; Bin MA

doi:10.11959/j.issn.2096-109x.2023064

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Review of malware detection and classification visualization techniques

Comprehensive Review | 更新时间：2024-06-03

- Review of malware detection and classification visualization techniques
- Chinese Journal of Network and Information Security Vol. 9, Issue 5, Pages: 1-20(2023)
- 作者机构：
  
  1. 南京信息工程大学数字取证教育部工程研究中心，江苏南京 210044
  2. 南京信息工程大学计算机学院，江苏南京 210044
  3. 数学工程与先进计算国家重点实验室，河南郑州 450001
  4. 中国科学技术大学网络空间安全学院，安徽合肥 230031
  5. 中国航天系统科学与工程研究院，北京 100048
  6. 信息工程大学，河南郑州 450001
  7. 齐鲁工业大学网络空间安全学院，山东济南 250353
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2023064
  CLC： TP309
- Online First：2023-10，
  
  Published：25 October 2023
- 稿件说明：
移动端阅览
Jinwei WANG, Zhengjia CHEN, Xue XIE, et al. Review of malware detection and classification visualization techniques[J]. Chinese Journal of Network and Information Security, 2023, 9(5): 1-20.
DOI：

Jinwei WANG, Zhengjia CHEN, Xue XIE, et al. Review of malware detection and classification visualization techniques[J]. Chinese Journal of Network and Information Security, 2023, 9(5): 1-20. DOI： 10.11959/j.issn.2096-109x.2023064.

摘要

随着科技的不断发展，恶意软件及其变种的种类不断增多，已经成为网络安全的一个巨大挑战。这些恶意软件采用了多样的技术手段，以欺骗或规避传统检测方法，从而使得传统非可视化检测技术不再适用。近年来，数据可视化作为一种强有力的恶意软件检测和分类手段引起了学术界的广泛关注。通过以图像的方式呈现恶意软件的核心特征，这类方法能够显著提高恶意软件的检测和分类准确率，从而在网络安全领域具有广阔的研究前景。综述恶意软件检测领域的传统非可视化检测技术与可视化检测技术。介绍了恶意软件检测的传统非可视化方式，包括静态检测、动态检测、混合检测；重点对一些常见的恶意软件可视化方法进行了调研和综合评述，主要包括可视化结合机器学习与可视化结合深度学习两大方向，这两种方法在恶意软件检测和分类中各有优势和特点，因此在选择检测和分类方法时，需要综合考虑多个因素，包括数据集大小、计算资源和时间限制、模型准确度以及实现复杂度等；对目前检测技术所面临的问题进行了总结，并对未来的发展方向进一步展望。

Abstract

With the rapid advancement of technology

network security faces a significant challenge due to the proliferation of malicious software and its variants.These malicious software use various technical tactics to deceive or bypass traditional detection methods

rendering conventional non-visual detection techniques inadequate.In recent years

data visualization has gained considerable attention in the academic community as a powerful approach for detecting and classifying malicious software.By visually representing the key features of malicious software

these methods greatly enhance the accuracy of malware detection and classification

opening up extensive research opportunities in the field of cyber security.An overview of traditional non-visual detection techniques and visualization-based methods were provided in the realm of malicious software detection.Traditional non-visual approaches for malicious software detection

including static analysis

dynamic analysis

and hybrid techniques

were introduced.Subsequently

a comprehensive survey and evaluation of prominent contemporary visualization-based methods for detecting malicious software were undertaken.This primarily encompasses encompassed the integration of visualization with machine learning and visualization combined with deep learning

each of which exhibits distinct advantages and characteristics within the domain of malware detection and classification.Consequently

the holistic consideration of several factors

such as dataset size

computational resources

time constraints

model accuracy

and implementation complexity

is necessary for the selection of detection and classification methods.In conclusion

the challenges currently faced by detection technologies are summarized

and a forward-looking perspective on future research directions in the field is provided.

关键词

Keywords

references

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