Android malware detection based on improved random forest
Aiming at the defect of vote principle in random forest algorithm which is incapable of distinguishing the differences between strong classifier and weak classifier,a weighted voting improved method was proposed,and an improved random forest classification (IRFCM) was proposed to detect Android malw...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2017-04-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017073/ |
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| Summary: | Aiming at the defect of vote principle in random forest algorithm which is incapable of distinguishing the differences between strong classifier and weak classifier,a weighted voting improved method was proposed,and an improved random forest classification (IRFCM) was proposed to detect Android malware on the basis of this method.The IRFCM chose Permission information and Intent information as attribute features from AndroidManifest.xml files and optimized them,then applied the model to classify the final feature vectors.The experimental results in Weka environment show that IRFCM has better classification accuracy and classification efficiency. |
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| ISSN: | 1000-436X |