Fuzzy–synthetic minority oversampling technique: Oversampling based on fuzzy set theory for Android malware detection in imbalanced datasets
In previous work, imbalanced datasets composed of more benign samples (the majority class) than the malicious one (the minority class) have been widely adopted in Android malware detection. These imbalanced datasets bias learning toward the majority class, so that the minority class examples are mor...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-04-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147717703116 |
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| _version_ | 1850223317532803072 |
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| author | Yanping Xu Chunhua Wu Kangfeng Zheng Xinxin Niu Yixian Yang |
| author_facet | Yanping Xu Chunhua Wu Kangfeng Zheng Xinxin Niu Yixian Yang |
| author_sort | Yanping Xu |
| collection | DOAJ |
| description | In previous work, imbalanced datasets composed of more benign samples (the majority class) than the malicious one (the minority class) have been widely adopted in Android malware detection. These imbalanced datasets bias learning toward the majority class, so that the minority class examples are more likely to be misclassified. To solve the problem, we propose a new oversampling method called fuzzy–synthetic minority oversampling technique, which is based on fuzzy set theory and the synthetic minority oversampling technique method. As the sample size of the majority class increases relative to that of the minority class, fuzzy–synthetic minority oversampling technique generates more synthetic examples for each minority class examples in the fuzzy region, where the minority examples have a low degree of membership to the minority class and are more likely to be misclassified. Using the new synthetic examples, the classifiers build larger decision regions that contain more minority examples, and they are no longer biased to the majority class. Compared with synthetic minority oversampling technique and Borderline–synthetic minority oversampling technique methods, fuzzy–synthetic minority oversampling technique achieves higher accuracy on both the minority class and the entire datasets. |
| format | Article |
| id | doaj-art-5d6b763dcfd54571a83cdccfb9dd3cd4 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2017-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-5d6b763dcfd54571a83cdccfb9dd3cd42025-08-20T02:06:00ZengWileyInternational Journal of Distributed Sensor Networks1550-14772017-04-011310.1177/1550147717703116Fuzzy–synthetic minority oversampling technique: Oversampling based on fuzzy set theory for Android malware detection in imbalanced datasetsYanping XuChunhua WuKangfeng ZhengXinxin NiuYixian YangIn previous work, imbalanced datasets composed of more benign samples (the majority class) than the malicious one (the minority class) have been widely adopted in Android malware detection. These imbalanced datasets bias learning toward the majority class, so that the minority class examples are more likely to be misclassified. To solve the problem, we propose a new oversampling method called fuzzy–synthetic minority oversampling technique, which is based on fuzzy set theory and the synthetic minority oversampling technique method. As the sample size of the majority class increases relative to that of the minority class, fuzzy–synthetic minority oversampling technique generates more synthetic examples for each minority class examples in the fuzzy region, where the minority examples have a low degree of membership to the minority class and are more likely to be misclassified. Using the new synthetic examples, the classifiers build larger decision regions that contain more minority examples, and they are no longer biased to the majority class. Compared with synthetic minority oversampling technique and Borderline–synthetic minority oversampling technique methods, fuzzy–synthetic minority oversampling technique achieves higher accuracy on both the minority class and the entire datasets.https://doi.org/10.1177/1550147717703116 |
| spellingShingle | Yanping Xu Chunhua Wu Kangfeng Zheng Xinxin Niu Yixian Yang Fuzzy–synthetic minority oversampling technique: Oversampling based on fuzzy set theory for Android malware detection in imbalanced datasets International Journal of Distributed Sensor Networks |
| title | Fuzzy–synthetic minority oversampling technique: Oversampling based on fuzzy set theory for Android malware detection in imbalanced datasets |
| title_full | Fuzzy–synthetic minority oversampling technique: Oversampling based on fuzzy set theory for Android malware detection in imbalanced datasets |
| title_fullStr | Fuzzy–synthetic minority oversampling technique: Oversampling based on fuzzy set theory for Android malware detection in imbalanced datasets |
| title_full_unstemmed | Fuzzy–synthetic minority oversampling technique: Oversampling based on fuzzy set theory for Android malware detection in imbalanced datasets |
| title_short | Fuzzy–synthetic minority oversampling technique: Oversampling based on fuzzy set theory for Android malware detection in imbalanced datasets |
| title_sort | fuzzy synthetic minority oversampling technique oversampling based on fuzzy set theory for android malware detection in imbalanced datasets |
| url | https://doi.org/10.1177/1550147717703116 |
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