Review of Android Malware Detection Based on Deep Learning

被引:35
|
作者
Wang, Zhiqiang [1 ,2 ]
Liu, Qian [1 ]
Chi, Yaping [1 ]
机构
[1] Beijing Elect Sci & Technol Inst, Dept Cyberspace Secur, Beijing 100071, Peoples R China
[2] State Informat Ctr, Beijing 100000, Peoples R China
基金
中国博士后科学基金;
关键词
Android; malware; deep learning; review; SELECTION; ATTACKS; SYSTEM;
D O I
10.1109/ACCESS.2020.3028370
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
At present, smartphones running the Android operating system have occupied the leading market share. However, due to the Android operating system's open-source nature, Android malware has increased dramatically. Malware can steal user privacy and even maliciously charge fees and steal funds. It has posed a severe threat to cyberspace security because traditional detection methods have many limitations. With the widespread application of deep learning in recent years, the method of detecting Android malware using deep learning has gradually attracted widespread attention from scholars at home and abroad. Although scholars have researched Android malware detection using deep learning, there is currently a lack of a detailed and comprehensive introduction to malware detection's latest research results based on deep learning. In order to solve this problem, this study analyzes and summarizes the latest research results by investigating a large number of the latest domestic and international academic papers, summarizing malware detection architecture and detection schemes, and analyzing existing problems and challenges. This review will help researchers better understand the research status and future research directions in this field.
引用
收藏
页码:181102 / 181126
页数:25
相关论文
共 50 条
  • [31] Android malware detection framework based on sensitive opcodes and deep reinforcement learning
    Yang, Jiyun
    Gui, Can
    [J]. Journal of Intelligent and Fuzzy Systems, 2024, 46 (04): : 8933 - 8942
  • [32] Android Malware Detection Using Machine Learning: A Review
    Chowdhury, Naseef-Ur-Rahman
    Haque, Ahshanul
    Soliman, Hamdy
    Hossen, Mohammad Sahinur
    Fatima, Tanjim
    Ahmed, Imtiaz
    [J]. INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 3, INTELLISYS 2023, 2024, 824 : 507 - 522
  • [33] An Android Malware Detection Method Based on Deep AutoEncoder
    He, Nengqiang
    Wang, Tianqi
    Chen, Pingyang
    Yan, Hanbing
    Jin, Zhengping
    [J]. PROCEEDINGS OF 2018 ARTIFICIAL INTELLIGENCE AND CLOUD COMPUTING CONFERENCE (AICCC 2018), 2018, : 88 - 93
  • [34] Static Analysis of Android Malware Detection using Deep Learning
    Sandeep, H. R.
    [J]. PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICCS), 2019, : 841 - 845
  • [35] PetaDroid: Adaptive Android Malware Detection Using Deep Learning
    Karbab, ElMouatez Billah
    Debbabi, Mourad
    [J]. DETECTION OF INTRUSIONS AND MALWARE, AND VULNERABILITY ASSESSMENT, DIMVA 2021, 2021, 12756 : 319 - 340
  • [36] Droid-Sec: Deep Learning in Android Malware Detection
    Yuan, Zhenlong
    Lu, Yongqiang
    Wang, Zhaoguo
    Xue, Yibo
    [J]. SIGCOMM'14: PROCEEDINGS OF THE 2014 ACM CONFERENCE ON SPECIAL INTEREST GROUP ON DATA COMMUNICATION, 2014, : 371 - 372
  • [37] Malware Detection in Android IoT Systems Using Deep Learning
    Waqar, Muhammad
    Fareed, Sabeeh
    Kim, Ajung
    Malik, Saif Ur Rehman
    Imran, Muhammad
    Yaseen, Muhammad Usman
    [J]. CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 74 (02): : 4399 - 4415
  • [38] DroidDetector: Android Malware Characterization and Detection Using Deep Learning
    Yuan, Zhenlong
    Lu, Yongqiang
    Xue, Yibo
    [J]. TSINGHUA SCIENCE AND TECHNOLOGY, 2016, 21 (01) : 114 - 123
  • [39] SHIELD: A Multimodal Deep Learning Framework for Android Malware Detection
    Singh, Narendra
    Tripathy, Somanath
    Bezawada, Bruhadeshwar
    [J]. INFORMATION SYSTEMS SECURITY, ICISS 2022, 2022, 13784 : 64 - 83
  • [40] Droid-Sec: Deep Learning in Android Malware Detection
    Yuan, Zhenlong
    Lu, Yongqiang
    Wang, Zhaoguo
    Xue, Yibo
    [J]. ACM SIGCOMM COMPUTER COMMUNICATION REVIEW, 2014, 44 (04) : 371 - 372