A Survey on Mobile Malware Detection Methods using Machine Learning

被引：17
作者：
Kambar, Mina Esmail Zadeh Nojoo [1 ]
Esmaeilzadeh, Armin [1 ]
Kim, Yoohwan [1 ]
Taghva, Kazem [1 ]
机构：
[1] Univ Nevada, Dept Comp Sci, Las Vegas, NV 89154 USA
来源：
2022 IEEE 12TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC) | 2022年
基金：
美国国家科学基金会;
关键词：
Mobile malware; Traffic detection; Dynamic Malware detection; Mobile security; SECURITY;
D O I：
10.1109/CCWC54503.2022.9720753
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
The prevalence of mobile devices (smartphones) along with the availability of high-speed internet access world-ide resulted in a wide variety of mobile applications that carry a large amount of confidential information. Although popular mobile operating systems such as iOS and Android constantly increase their defenses methods, data shows that the number of intrusions and attacks using mobile applications is rising continuously. Experts use techniques to detect malware before the malicious application gets installed, during the runtime or by the network traffic analysis. In this paper, we first present the information about different categories of mobile malware and threats; then, we classify the recent research methods on mobile malware traffic detection.
引用
收藏
页码：215 / 221
页数：7
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