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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| _version_ | 1841539487087198208 |
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| author | Hong-yu YANG Jin XU |
| author_facet | Hong-yu YANG Jin XU |
| author_sort | Hong-yu YANG |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2a1862f6e6d04a31ab266c8cf6497d94 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2017-04-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-2a1862f6e6d04a31ab266c8cf6497d942025-01-14T07:11:57ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2017-04-013881659708923Android malware detection based on improved random forestHong-yu YANGJin XUAiming 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017073/random forestweighted votemalwareclassification detection |
| spellingShingle | Hong-yu YANG Jin XU Android malware detection based on improved random forest Tongxin xuebao random forest weighted vote malware classification detection |
| title | Android malware detection based on improved random forest |
| title_full | Android malware detection based on improved random forest |
| title_fullStr | Android malware detection based on improved random forest |
| title_full_unstemmed | Android malware detection based on improved random forest |
| title_short | Android malware detection based on improved random forest |
| title_sort | android malware detection based on improved random forest |
| topic | random forest weighted vote malware classification detection |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017073/ |
| work_keys_str_mv | AT hongyuyang androidmalwaredetectionbasedonimprovedrandomforest AT jinxu androidmalwaredetectionbasedonimprovedrandomforest |