A Survey on Mobile Malware Detection Methods using Machine Learning

被引:14
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
相关论文
共 73 条
  • [1] Abdolazimi R., 2022, 2022 IEEE 12 ANN COM
  • [2] Ahvanooey MT, 2017, INT J ADV COMPUT SC, V8, P30, DOI 10.14569/IJACSA.2017.081005
  • [3] [Anonymous], 2021, MALWARE EVOLUTION PC
  • [4] [Anonymous], MALWARE TRAFFIC ANAL
  • [5] [Anonymous], 2019, KOODOUS MALWARE DATA
  • [6] [Anonymous], 2017, The Judy Malware: Possibly the largest malware campaign found on Google Play,"
  • [7] PermPair: Android Malware Detection Using Permission Pairs
    Arora, Anshul
    Peddoju, Sateesh K.
    Conti, Mauro
    [J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2020, 15 : 1968 - 1982
  • [8] Hybrid Android Malware Detection by Combining Supervised and Unsupervised Learning
    Arora, Anshul
    Peddoju, Sateesh K.
    Chouhan, Vikas
    Chaudhary, Ajay
    [J]. MOBICOM'18: PROCEEDINGS OF THE 24TH ANNUAL INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND NETWORKING, 2018, : 798 - 800
  • [9] Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
    Arp, Daniel
    Spreitzenbarth, Michael
    Huebner, Malte
    Gascon, Hugo
    Rieck, Konrad
    [J]. 21ST ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2014), 2014,
  • [10] N-Gram, Semantic-Based Neural Network for Mobile Malware Network Traffic Detection
    Bai, Huiwen
    Liu, Guangjie
    Liu, Weiwei
    Quan, Yingxue
    Huang, Shuhua
    [J]. SECURITY AND COMMUNICATION NETWORKS, 2021, 2021