ImageDroid: Using deep learning to efficiently detect Android malware and automatically mark malicious features
The popularity of the Android platform has led to an explosion in malware. The current
research on Android malware mainly focuses on malware detection or malware family …
research on Android malware mainly focuses on malware detection or malware family …
An Android Malware Detection Model Based on DT‐SVM
M Yang, X Chen, Y Luo, H Zhang - Security and …, 2020 - Wiley Online Library
In order to improve the accuracy and efficiency of Android malware detection, an Android
malware detection model based on decision tree (DT) with support vector machine (SVM) …
malware detection model based on decision tree (DT) with support vector machine (SVM) …
DCEL: Classifier Fusion Model for Android Malware Detection
X Xu, S Jiang, J Zhao, X Wang - Journal of Systems …, 2024 - ieeexplore.ieee.org
The rapid growth of mobile applications, the popularity of the Android system and its
openness have attracted many hackers and even criminals, who are creating lots of Android …
openness have attracted many hackers and even criminals, who are creating lots of Android …
[PDF][PDF] SPRD: 基于应用UI 和程序依赖图的Android 重打包应用快速检测方法
汪润， 王丽娜， 唐奔宵， 赵磊 - Journal on Communications, 2018 - infocomm-journal.com
研究发现重打包应用通常不修改应用用户交互界面(UI, user interface) 的结构,
提出一种基于应用UI 和程序代码的两阶段检测方法. 首先, 设计了一种基于UI …
提出一种基于应用UI 和程序代码的两阶段检测方法. 首先, 设计了一种基于UI …
[PDF][PDF] DeepRD: 基于Siamese LSTM 网络的Android 重打包应用检测方法
汪润， 唐奔宵， 王丽娜 - Journal on Communications, 2018 - wangrun.github.io
目前, Android 平台重打包应用检测方法依赖于专家定义特征, 不但耗时耗力,
而且其特征容易被攻击者猜测. 另外, 现有的应用特征表示难以在常见的重打包应用类型检测中 …
而且其特征容易被攻击者猜测. 另外, 现有的应用特征表示难以在常见的重打包应用类型检测中 …
基于CNN 的Android 恶意代码检测方法.
赖英旭， 陈摇业， 罗叶红… - Journal of Beijing …, 2020 - search.ebscohost.com
摘摇要: 针对传统Android 恶意应用检测技术无法对当前爆发增长的恶意应用进行高效检测,
对移动终端安全造成严重威胁的问题, 利用深度学习中卷积神经网络(convolutional neural …
对移动终端安全造成严重威胁的问题, 利用深度学习中卷积神经网络(convolutional neural …