TY - JOUR
T1 - Android malware detection based on sensitive features combination
AU - Yao, Xuanxia
AU - Li, Yang
AU - Shi, Zhiguo
AU - Liu, Kaijun
AU - Du, Xiao Jiang
N1 - Publisher Copyright:
© 2023 John Wiley & Sons, Ltd.
PY - 2023/3/10
Y1 - 2023/3/10
N2 - With the development of mobile communication, Android software has increased sharply. Meanwhile, more and more malware emerges. Identifying malware in time is very important. Currently, most malware identifying methods are static, and the detection accuracy mainly depends on the classification feature and the algorithm. In order to improve the detection accuracy, reducing the dimension and difficulty of feature extraction, we propose a lightweight Android malware detection method based on sensitive features combination. After fully analyzing the static features in Android software, we improve the extraction methods of various features, define four sensitive features, and then form a sensitive features combination to more accurately reflect the characteristics of Android software with fewer features. Finally, four different machine learning classification algorithms were used to evaluate the classification effect of the sensitive features combination. The experiments show that the sensitive features combination has a good classification effect. When combined with the random forest classification algorithm, the accuracy is the highest, which could reach 97.6%.
AB - With the development of mobile communication, Android software has increased sharply. Meanwhile, more and more malware emerges. Identifying malware in time is very important. Currently, most malware identifying methods are static, and the detection accuracy mainly depends on the classification feature and the algorithm. In order to improve the detection accuracy, reducing the dimension and difficulty of feature extraction, we propose a lightweight Android malware detection method based on sensitive features combination. After fully analyzing the static features in Android software, we improve the extraction methods of various features, define four sensitive features, and then form a sensitive features combination to more accurately reflect the characteristics of Android software with fewer features. Finally, four different machine learning classification algorithms were used to evaluate the classification effect of the sensitive features combination. The experiments show that the sensitive features combination has a good classification effect. When combined with the random forest classification algorithm, the accuracy is the highest, which could reach 97.6%.
KW - Android
KW - malware detection
KW - multi-feature
KW - sensitive feature
UR - http://www.scopus.com/inward/record.url?scp=85147096824&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85147096824&partnerID=8YFLogxK
U2 - 10.1002/cpe.7555
DO - 10.1002/cpe.7555
M3 - Article
AN - SCOPUS:85147096824
SN - 1532-0626
VL - 35
SP - 1
JO - Concurrency and Computation: Practice and Experience
JF - Concurrency and Computation: Practice and Experience
IS - 6
ER -