Feature selection‐based android malware adversarial sample generation and detection method
Abstract With the popularisation of Android smartphones, the value of mobile application security research has increased. The emergence of adversarial technology makes it possible for malware to evade detection. Therefore, research is conducted on Android malicious applications of adversarial attack...
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| Format: | Article |
| Language: | English |
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Wiley
2021-11-01
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| Series: | IET Information Security |
| Online Access: | https://doi.org/10.1049/ise2.12030 |
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doaj-728c020b100e48d3abe8f167ac61b1222021-10-05T10:24:22ZengWileyIET Information Security1751-87091751-87172021-11-0115640141610.1049/ise2.12030Feature selection‐based android malware adversarial sample generation and detection methodXiangjun Li0Ke Kong1Su Xu2Pengtao Qin3Daojing He4School of Software Nanchang University Nanchang Jiangxi ChinaSchool of Information Engineering Nanchang University Nanchang Jiangxi ChinaSchool of Information Engineering Nanchang University Nanchang Jiangxi ChinaSchool of Information Engineering Nanchang University Nanchang Jiangxi ChinaSchool of Software Nanchang University Nanchang Jiangxi ChinaAbstract With the popularisation of Android smartphones, the value of mobile application security research has increased. The emergence of adversarial technology makes it possible for malware to evade detection. Therefore, research is conducted on Android malicious applications of adversarial attack. To clarify the process and theory of adversarial sample generation, an adversarial sample generation algorithm is proposed that filters features based on feature spatial distribution and definition. These features are modified on real malicious samples to form adversarial samples. In addition, to enhance the robustness of adversarial sample classification detection, a multiple feature set detection algorithm is designed and implemented. Using the frequency differential enhancement feature selection algorithm to perform feature screening, the algorithm forms two different feature sets and establishes two different training sets to train different classification algorithms. Prediction results obtained by the two classification algorithms are integrated based on certain rules. Experimental results on the VirusShare dataset show that both algorithms are effective. The detection results in an actual environment also prove the effectiveness of the multiple feature set detection algorithm.https://doi.org/10.1049/ise2.12030 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Xiangjun Li Ke Kong Su Xu Pengtao Qin Daojing He |
| spellingShingle |
Xiangjun Li Ke Kong Su Xu Pengtao Qin Daojing He Feature selection‐based android malware adversarial sample generation and detection method IET Information Security |
| author_facet |
Xiangjun Li Ke Kong Su Xu Pengtao Qin Daojing He |
| author_sort |
Xiangjun Li |
| title |
Feature selection‐based android malware adversarial sample generation and detection method |
| title_short |
Feature selection‐based android malware adversarial sample generation and detection method |
| title_full |
Feature selection‐based android malware adversarial sample generation and detection method |
| title_fullStr |
Feature selection‐based android malware adversarial sample generation and detection method |
| title_full_unstemmed |
Feature selection‐based android malware adversarial sample generation and detection method |
| title_sort |
feature selection‐based android malware adversarial sample generation and detection method |
| publisher |
Wiley |
| series |
IET Information Security |
| issn |
1751-8709 1751-8717 |
| publishDate |
2021-11-01 |
| description |
Abstract With the popularisation of Android smartphones, the value of mobile application security research has increased. The emergence of adversarial technology makes it possible for malware to evade detection. Therefore, research is conducted on Android malicious applications of adversarial attack. To clarify the process and theory of adversarial sample generation, an adversarial sample generation algorithm is proposed that filters features based on feature spatial distribution and definition. These features are modified on real malicious samples to form adversarial samples. In addition, to enhance the robustness of adversarial sample classification detection, a multiple feature set detection algorithm is designed and implemented. Using the frequency differential enhancement feature selection algorithm to perform feature screening, the algorithm forms two different feature sets and establishes two different training sets to train different classification algorithms. Prediction results obtained by the two classification algorithms are integrated based on certain rules. Experimental results on the VirusShare dataset show that both algorithms are effective. The detection results in an actual environment also prove the effectiveness of the multiple feature set detection algorithm. |
| url |
https://doi.org/10.1049/ise2.12030 |
| work_keys_str_mv |
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1716843345977802752 |