Mining behavior pattern of mobile malware with convolutional neural network
The features extracted by existing malicious Android application detection methods are redundant and too abstract to reflect the behavior patterns of malicious applications in high-level semantics.In order to solve this problem,an interpretable detection method was proposed.Suspicious system call co...
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2020-12-01
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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.2020073 |
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| _version_ | 1841530020203331584 |
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| author | Xin ZHANG Weizhong QIANG Yueming WU Deqing ZOU Hai JIN |
| author_facet | Xin ZHANG Weizhong QIANG Yueming WU Deqing ZOU Hai JIN |
| author_sort | Xin ZHANG |
| collection | DOAJ |
| description | The features extracted by existing malicious Android application detection methods are redundant and too abstract to reflect the behavior patterns of malicious applications in high-level semantics.In order to solve this problem,an interpretable detection method was proposed.Suspicious system call combinations clustering by social network analysis was converted to a single channel image.Convolution neural network was applied to classify Android application.The model trained was used to find the most suspicious system call combinations by convolution layer gradient weight classification activation mapping algorithm,thus mining and understanding malicious application behavior.The experimental results show that the method can correctly discover the behavior patterns of malicious applications on the basis of efficient detection. |
| format | Article |
| id | doaj-art-f724fc1627c94bb5b0b57a383bffa613 |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2020-12-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-f724fc1627c94bb5b0b57a383bffa6132025-01-15T03:14:28ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2020-12-016354459561584Mining behavior pattern of mobile malware with convolutional neural networkXin ZHANGWeizhong QIANGYueming WUDeqing ZOUHai JINThe features extracted by existing malicious Android application detection methods are redundant and too abstract to reflect the behavior patterns of malicious applications in high-level semantics.In order to solve this problem,an interpretable detection method was proposed.Suspicious system call combinations clustering by social network analysis was converted to a single channel image.Convolution neural network was applied to classify Android application.The model trained was used to find the most suspicious system call combinations by convolution layer gradient weight classification activation mapping algorithm,thus mining and understanding malicious application behavior.The experimental results show that the method can correctly discover the behavior patterns of malicious applications on the basis of efficient detection.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2020073Androidrapid detectionconvolutional neural networksocial network analysis |
| spellingShingle | Xin ZHANG Weizhong QIANG Yueming WU Deqing ZOU Hai JIN Mining behavior pattern of mobile malware with convolutional neural network 网络与信息安全学报 Android rapid detection convolutional neural network social network analysis |
| title | Mining behavior pattern of mobile malware with convolutional neural network |
| title_full | Mining behavior pattern of mobile malware with convolutional neural network |
| title_fullStr | Mining behavior pattern of mobile malware with convolutional neural network |
| title_full_unstemmed | Mining behavior pattern of mobile malware with convolutional neural network |
| title_short | Mining behavior pattern of mobile malware with convolutional neural network |
| title_sort | mining behavior pattern of mobile malware with convolutional neural network |
| topic | Android rapid detection convolutional neural network social network analysis |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2020073 |
| work_keys_str_mv | AT xinzhang miningbehaviorpatternofmobilemalwarewithconvolutionalneuralnetwork AT weizhongqiang miningbehaviorpatternofmobilemalwarewithconvolutionalneuralnetwork AT yuemingwu miningbehaviorpatternofmobilemalwarewithconvolutionalneuralnetwork AT deqingzou miningbehaviorpatternofmobilemalwarewithconvolutionalneuralnetwork AT haijin miningbehaviorpatternofmobilemalwarewithconvolutionalneuralnetwork |