Review of malware detection and classification visualization techniques
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 detect...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2023-10-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023064 |
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| _version_ | 1841529633345896448 |
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| author | Jinwei WANG Zhengjia CHEN Xue XIE Xiangyang LUO Bin MA |
| author_facet | Jinwei WANG Zhengjia CHEN Xue XIE Xiangyang LUO Bin MA |
| author_sort | Jinwei WANG |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a184031d944443f7ac18512eb881153e |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2023-10-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-a184031d944443f7ac18512eb881153e2025-01-15T03:16:57ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2023-10-01912059581178Review of malware detection and classification visualization techniquesJinwei WANGZhengjia CHENXue XIEXiangyang LUOBin MAWith 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.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023064machine learningdeep learningdata visualizationmalware detection and classification |
| spellingShingle | Jinwei WANG Zhengjia CHEN Xue XIE Xiangyang LUO Bin MA Review of malware detection and classification visualization techniques 网络与信息安全学报 machine learning deep learning data visualization malware detection and classification |
| title | Review of malware detection and classification visualization techniques |
| title_full | Review of malware detection and classification visualization techniques |
| title_fullStr | Review of malware detection and classification visualization techniques |
| title_full_unstemmed | Review of malware detection and classification visualization techniques |
| title_short | Review of malware detection and classification visualization techniques |
| title_sort | review of malware detection and classification visualization techniques |
| topic | machine learning deep learning data visualization malware detection and classification |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023064 |
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