Open AccessFeature selection‐based android malware adversarial sample generation and detection method
Author(s) -
Li Xiangjun,
Kong Ke,
Xu Su,
Qin Pengtao,
He Daojing
Publication year - 2021
Publication title -
iet information security
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 34
eISSN - 1751-8717
pISSN - 1751-8709
DOI - 10.1049/ise2.12030
Subject(s) - adversarial system , feature selection , computer science , malware , artificial intelligence , pattern recognition (psychology) , android malware , data mining , robustness (evolution) , feature extraction , android (operating system) , machine learning , computer security , operating system , biochemistry , chemistry , gene
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.