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
  • [21] ANDROID MALWARE DETECTION USING THE DENDRITIC CELL ALGORITHM
    Ng, Deniel V.
    Hwang, Jen-Ing G.
    PROCEEDINGS OF 2014 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 1, 2014, : 257 - 262
  • [22] Deep Feature Extraction and Classification of Android Malware Images
    Singh, Jaiteg
    Thakur, Deepak
    Ali, Farman
    Gera, Tanya
    Kwak, Kyung Sup
    SENSORS, 2020, 20 (24) : 1 - 29
  • [23] A Robust Malware Detection Approach for Android System against Adversarial Example Attacks
    Li, Wenjia
    Bala, Neha
    Ahmar, Aemun
    Tovar, Fernanda
    Battu, Arpit
    Bambarkar, Prachi
    2019 IEEE 5TH INTERNATIONAL CONFERENCE ON COLLABORATION AND INTERNET COMPUTING (CIC 2019), 2019, : 360 - 365
  • [24] RGDroid: Detecting Android Malware with Graph Convolutional Networks against Structural Attacks
    Li, Yakang
    Hu, Yikun
    Wang, Yizhuo
    He, Yituo
    Lu, Haining
    Gu, Dawu
    2023 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION AND REENGINEERING, SANER, 2023, : 639 - 650
  • [25] Composition-malware: building Android malware at run time
    Canfora, Gerardo
    Mercaldo, Francesco
    Moriano, Giovanni
    Visaggio, Corrado Aaron
    PROCEEDINGS 10TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY AND SECURITY ARES 2015, 2015, : 318 - 326
  • [26] Darwinian Malware Detectors: A Comparison of Evolutionary Solutions to Android Malware
    Wilkins, Zachary
    Zincir-Heywood, Nur
    PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCCO'19 COMPANION), 2019, : 1651 - 1658
  • [27] A Review of Attacks, Vulnerabilities, and Defenses in Industry 4.0 with New Challenges on Data Sovereignty Ahead
    Pedreira, Vitor
    Barros, Daniel
    Pinto, Pedro
    SENSORS, 2021, 21 (15)
  • [28] EAGLE: Evasion Attacks Guided by Local Explanations Against Android Malware Classification
    Shu, Zhan
    Yan, Guanhua
    IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 2024, 21 (04) : 3165 - 3182
  • [29] How to Tame Your Android Malware
    Burke, Ivan
    Pieterse, Heloise
    PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON CYBER WARFARE AND SECURITY (ICCWS-2015), 2015, : 54 - 65
  • [30] Static and Dynamic Analysis of Android Malware
    Kapratwar, Ankita
    Di Troia, Fabio
    Stamp, Mark
    ICISSP: PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY, 2017, : 653 - 662