DeepRD:LSTM-based Siamese network for Android repackaged applications detection
The state-of-art techniques in Android repackaging detection relied on experts to define features,however,these techniques were not only labor-intensive and time-consuming,but also the features were easily guessed by attackers.Moreover,the feature representation of applications which defined by expe...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2018-08-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018148/ |
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| author | Run WANG Benxiao TANG Li’na WANG |
| author_facet | Run WANG Benxiao TANG Li’na WANG |
| author_sort | Run WANG |
| collection | DOAJ |
| description | The state-of-art techniques in Android repackaging detection relied on experts to define features,however,these techniques were not only labor-intensive and time-consuming,but also the features were easily guessed by attackers.Moreover,the feature representation of applications which defined by experts cannot perform well to the common types of repackaging detection,which caused a high false negative rate in the real detection scenario.A deep learning-based repackaged applications detection approach was proposed to learn the program semantic features automatically for addressing the above two issues.Firstly,control and data flow analysis were taken for applications to form a sequence feature representation.Secondly,the sequence features were transformed into vectors based on word embedding model to train a Siamese LSTM network for automatically program feature learning.Finally,repackaged applications were detected based on the similarity measurement of learned program features.Experimental results show that the proposed approach achieves a precision of 95.7% and false negative rate of 6.2% in an open sourced dataset AndroZoo. |
| format | Article |
| id | doaj-art-2c3d4a6d93c74e2597d1b1160cf56aae |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2018-08-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-2c3d4a6d93c74e2597d1b1160cf56aae2025-01-14T07:15:16ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2018-08-0139698259719961DeepRD:LSTM-based Siamese network for Android repackaged applications detectionRun WANGBenxiao TANGLi’na WANGThe state-of-art techniques in Android repackaging detection relied on experts to define features,however,these techniques were not only labor-intensive and time-consuming,but also the features were easily guessed by attackers.Moreover,the feature representation of applications which defined by experts cannot perform well to the common types of repackaging detection,which caused a high false negative rate in the real detection scenario.A deep learning-based repackaged applications detection approach was proposed to learn the program semantic features automatically for addressing the above two issues.Firstly,control and data flow analysis were taken for applications to form a sequence feature representation.Secondly,the sequence features were transformed into vectors based on word embedding model to train a Siamese LSTM network for automatically program feature learning.Finally,repackaged applications were detected based on the similarity measurement of learned program features.Experimental results show that the proposed approach achieves a precision of 95.7% and false negative rate of 6.2% in an open sourced dataset AndroZoo.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018148/repackagingdeep learningsiamese networkLSTMsecurity and privacy |
| spellingShingle | Run WANG Benxiao TANG Li’na WANG DeepRD:LSTM-based Siamese network for Android repackaged applications detection Tongxin xuebao repackaging deep learning siamese network LSTM security and privacy |
| title | DeepRD:LSTM-based Siamese network for Android repackaged applications detection |
| title_full | DeepRD:LSTM-based Siamese network for Android repackaged applications detection |
| title_fullStr | DeepRD:LSTM-based Siamese network for Android repackaged applications detection |
| title_full_unstemmed | DeepRD:LSTM-based Siamese network for Android repackaged applications detection |
| title_short | DeepRD:LSTM-based Siamese network for Android repackaged applications detection |
| title_sort | deeprd lstm based siamese network for android repackaged applications detection |
| topic | repackaging deep learning siamese network LSTM security and privacy |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018148/ |
| work_keys_str_mv | AT runwang deeprdlstmbasedsiamesenetworkforandroidrepackagedapplicationsdetection AT benxiaotang deeprdlstmbasedsiamesenetworkforandroidrepackagedapplicationsdetection AT linawang deeprdlstmbasedsiamesenetworkforandroidrepackagedapplicationsdetection |