A Review on Android Malware: Attacks, Countermeasures and Challenges Ahead

被引:0
|
作者
Selvaganapathy S.G. [1 ]
Sadasivam S. [2 ]
Ravi V. [3 ]
机构
[1] Department of Information Technology, PSG College of Technology, Coimbatore
[2] Department of Computer Science and Engineering, PSG College of Technology, Coimbatore
[3] Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar
来源
Journal of Cyber Security and Mobility | 2021年 / 10卷 / 01期
关键词
adversarial attack; android; anomaly detection; attacks; defense; evasion attack; Malware; obfuscation attack;
D O I
10.13052/jcsm2245-1439.1017
中图分类号
学科分类号
摘要
Smartphones usage have become ubiquitous in modern life serving as a double-edged sword with opportunities and challenges in it. Along with the benefits, smartphones also have high exposure to malware. Malware has progressively penetrated thereby causing more turbulence. Malware authors have become increasingly sophisticated and are able to evade detection by anti-malware engines. This has led to a constant arms race between malware authors and malware defenders. This survey converges on Android malware and covers a walkthrough of the various obfuscation attacks deployed during malware analysis phase along with the myriad of adversarial attacks operated at malware detection phase. The review also unscrambles the difficulties currently faced in deploying an on-device, lightweight malware detector. It sheds spotlight for researchers to perceive the current state of the art techniques available to fend off malware along with suggestions on possible future directions. © 2021 River Publishers
引用
收藏
页码:177 / 230
页数:53
相关论文
共 50 条
  • [1] Android Malware Attacks and Countermeasures: Current and Future Directions
    Raveendranath, Rahul
    Rajamani, Venkiteswaran
    Babu, Anoop Joseph
    Datta, Soumya Kanti
    2014 INTERNATIONAL CONFERENCE ON CONTROL, INSTRUMENTATION, COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICCICCT), 2014, : 137 - 143
  • [2] A Defensive Strategy Against Android Adversarial Malware Attacks
    Atedjio, Fabrice Setephin
    Lienou, Jean-Pierre
    Nelson, Frederica F.
    Shetty, Sachin S.
    Kamhoua, Charles A.
    IEEE ACCESS, 2024, 12 : 169432 - 169441
  • [3] Review of Android Malware Detection Based on Deep Learning
    Wang, Zhiqiang
    Liu, Qian
    Chi, Yaping
    IEEE ACCESS, 2020, 8 : 181102 - 181126
  • [4] Stealth attacks: An extended insight into the obfuscation effects on Android malware
    Maiorca, Davide
    Ariu, Davide
    Corona, Igino
    Aresu, Marco
    Giacinto, Giorgio
    COMPUTERS & SECURITY, 2015, 51 : 16 - 31
  • [5] DroidEnemy: Battling adversarial example attacks for Android malware detection
    Bala, Neha
    Ahmar, Aemun
    Li, Wenjia
    Tovar, Fernanda
    Battu, Arpit
    Bambarkar, Prachi
    DIGITAL COMMUNICATIONS AND NETWORKS, 2022, 8 (06) : 1040 - 1047
  • [6] A Review on Malware Analysis for IoT and Android System
    Yadav C.S.
    Gupta S.
    SN Computer Science, 4 (2)
  • [7] The Android Malware Static Analysis: Techniques, Limitations, and Open Challenges
    Bakour, Khaled
    Unver, H. Murat
    Ghanem, Razan
    2018 3RD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK), 2018, : 586 - 593
  • [8] A review of cloned mobile malware applications for android devices
    Baykara, Muhammet
    Colak, Eren
    2018 6TH INTERNATIONAL SYMPOSIUM ON DIGITAL FORENSIC AND SECURITY (ISDFS), 2018, : 394 - 398
  • [9] Deep Learning for Android Malware Defenses: A Systematic Literature Review
    Liu, Yue
    Tantithamthavorn, Chakkrit
    Li, Li
    Liu, Yepang
    ACM COMPUTING SURVEYS, 2023, 55 (08)
  • [10] An Adversarial Machine Learning Model Against Android Malware Evasion Attacks
    Chen, Lingwei
    Hou, Shifu
    Ye, Yanfang
    Chen, Lifei
    WEB AND BIG DATA, 2017, 10612 : 43 - 55