.png){kind=icon}
返回
SemiDroid: a behavioral malware detector based on unsupervised machine learning techniques using feature selection approaches
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-020-01238-9.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the exponential growth in Android apps, Android based devices are becoming victims of target attackers in the silent battle of cybernetics. To protect Android based devices from malware has become more complex and crucial for academicians and researchers. The main vulnerability lies in the underlying permission model of Android apps. Android apps demand permission or permission sets at the time of their installation. In this study, we consider permission and API calls as features that help in developing a model for malware detection. To select appropriate features or feature sets from thirty different categories of Android apps, we implemented ten distinct feature selection approaches. With the help of selected feature sets we developed distinct models by using five different unsupervised machine learning algorithms. We conduct an experiment on 5,00,000 distinct Android apps which belongs to thirty distinct categories. Empirical results reveals that the model build by considering rough set analysis as a feature selection approach, and farthest first as a machine learning algorithm achieved the highest detection rate of 98.8% to detect malware from real-world apps.
Keyword:
Android apps
Permissions model
API calls
Unsupervised
Feature selection
Intrusion detection
Cyber security
Smartphone
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.7
论文数:
3.2K
被引数:
5.6K
机构
引用论文
A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Directed Self‐Assembly of Chiral, Optically Active Macrocyclic Tetranuclear Molecular Squares手性，光学活性大环四核分子正方形的定向自组装
PREAI
分享收藏
查看更多内容