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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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 1000-436X |