.png){kind=icon}
Return
Android Malware Detection Using Deep Learning
.png){kind=icon}
OA
AI
DOI：10.1016/j.procs.2021.03.106.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The Android operating system ranks first in the market share due to the system's smooth handling and many other features that it provides to Android users, which has attracted cyber criminals. Traditional Android malware detection methods, such as signature-based methods or methods monitoring battery consumption, may fail to detect recent malware. Therefore, we present a novel method for detecting malware in Android applications using Gated Recurrent Unit (GRU), which is a type of Recurrent Neural Network (RNN). We extract two static features, namely, Application Programming Interface (API) calls and Permissions from Android applications. We train and test our approach using CICAndMa12017 dataset. The experimental results show that our deep learning method outperforms several methods with accuracy of 98.2%. (C) 2021 The Authors. Published by Elsevier B.V.
Keywords:
Android Malware
Static analysis
API-calls
Permissions
Gated Recurrent Unit
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
ShareSaveShareSave
View more