Homology analysis of malware based on graph
Malware detection and homology analysis has been the hotspot of malware analysis.API call graph of malware can represent the behavior of it.Because of the subgraph isomorphism algorithm has high complexity,the analysis of malware based on the graph structure with low efficiency.Therefore,this studie...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2017-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017259/ |
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| _version_ | 1841539410627133440 |
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| author | Bing-lin ZHAO Xi MENG Jin HAN Jing WANG Fu-dong LIU |
| author_facet | Bing-lin ZHAO Xi MENG Jin HAN Jing WANG Fu-dong LIU |
| author_sort | Bing-lin ZHAO |
| collection | DOAJ |
| description | Malware detection and homology analysis has been the hotspot of malware analysis.API call graph of malware can represent the behavior of it.Because of the subgraph isomorphism algorithm has high complexity,the analysis of malware based on the graph structure with low efficiency.Therefore,this studies a homology analysis method of API graph of malware that use convolutional neural network.By selecting the key nodes,and construct neighborhood receptive field,the convolution neural network can handle graph structure data.Experimental results on 8 real-world malware family,shows that the accuracy rate of homology malware analysis achieves 93%,and the accuracy rate of the detection of malicious code to 96%. |
| format | Article |
| id | doaj-art-274c8c45a57f4c0188db267e82294a1f |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2017-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-274c8c45a57f4c0188db267e82294a1f2025-01-14T07:13:54ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2017-11-0138869359715331Homology analysis of malware based on graphBing-lin ZHAOXi MENGJin HANJing WANGFu-dong LIUMalware detection and homology analysis has been the hotspot of malware analysis.API call graph of malware can represent the behavior of it.Because of the subgraph isomorphism algorithm has high complexity,the analysis of malware based on the graph structure with low efficiency.Therefore,this studies a homology analysis method of API graph of malware that use convolutional neural network.By selecting the key nodes,and construct neighborhood receptive field,the convolution neural network can handle graph structure data.Experimental results on 8 real-world malware family,shows that the accuracy rate of homology malware analysis achieves 93%,and the accuracy rate of the detection of malicious code to 96%.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017259/malwarehomology analysisAPI call graphconvolutional neural network |
| spellingShingle | Bing-lin ZHAO Xi MENG Jin HAN Jing WANG Fu-dong LIU Homology analysis of malware based on graph Tongxin xuebao malware homology analysis API call graph convolutional neural network |
| title | Homology analysis of malware based on graph |
| title_full | Homology analysis of malware based on graph |
| title_fullStr | Homology analysis of malware based on graph |
| title_full_unstemmed | Homology analysis of malware based on graph |
| title_short | Homology analysis of malware based on graph |
| title_sort | homology analysis of malware based on graph |
| topic | malware homology analysis API call graph convolutional neural network |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017259/ |
| work_keys_str_mv | AT binglinzhao homologyanalysisofmalwarebasedongraph AT ximeng homologyanalysisofmalwarebasedongraph AT jinhan homologyanalysisofmalwarebasedongraph AT jingwang homologyanalysisofmalwarebasedongraph AT fudongliu homologyanalysisofmalwarebasedongraph |