检索结果-南通市图书馆

Using Capsule Networks for Android Malware Detection Through Orientation-Based Features

Computers, Materials & Continua 2022年第3期70卷 5345-5362页

作者： Sohail Khan Mohammad Nauman Suleiman Ali Alsaif Toqeer Ali Syed hassan ahmad eleraky Deanship of Preparatory Year and Supporting Studies Computer Science DepartmentImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia National University of Computer and Emerging Sciences Pakistan Department of Computer Science Islamic University of MedinaMedinaSaudi Arabia

Mobile phones are an essential part of modern *** two popular mobile phone platforms,Android and iPhone Operating System(iOS),have an immense impact on the lives of millions of *** these two,Android currently boasts more than 84%market ***,any personal data put on it are at great risk if not properly *** the other hand,more than a million pieces of malware have been reported on Android in just 2021 till *** and mitigating all this malware is extremely difficult for any set of human *** to this reason,machine learning-and specifically deep learning-has been utilized in the recent past to resolve this ***,deep learning models have primarily been designed for image *** this line of research has shown promising results,it has been difficult to really understand what the features extracted by deep learning models are in the domain of ***,due to the translation invariance property of popular models based on ConvolutionalNeural Network(CNN),the true potential of deep learning for malware analysis is yet to be *** resolve this issue,we envision the use of Capsule Networks(CapsNets),a state-of-the-art model in deep *** argue that since CapsNets are orientation-based in terms of images,they can potentially be used to capture spatial relationships between different features at different locations within a sequence of *** design a deep learning-based architecture that efficiently and effectively handles very large scale malware datasets to detect Androidmalware without resorting to very deep *** leads tomuch faster detection as well as increased *** achieve state-of-the-art F1 score of 0.987 with an FPR of just 0.002 for three very large,real-world malware *** code is made available as open source and can be used to further enhance our work with minimal effort.

关键词： Malware security Android deep learning capsule networks

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：