DroidMalwareDetector: A novel Android malware detection framework based on convolutional neural network

作者：

Highlights：

• The accuracy of the proposed model was calculated as high as 0.9.

• A novel 1-dimensional CNN model was proposed.

• The features were automatically selected thanks to the proposed model.

• The experiments were conducted on the de facto datasets.

• We shed light on the insights of Android malware through the conducted experiments.

摘要

•The accuracy of the proposed model was calculated as high as 0.9.•A novel 1-dimensional CNN model was proposed.•The features were automatically selected thanks to the proposed model.•The experiments were conducted on the de facto datasets.•We shed light on the insights of Android malware through the conducted experiments.

论文关键词：Android,Android malware detection,Deep neural network,Convolutional neural network,Mobile security

论文评审过程：Received 5 September 2020, Revised 29 April 2022, Accepted 8 June 2022, Available online 13 June 2022, Version of Record 15 June 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117833