The rise of obfuscated Android malware and impacts on detection methods

被引:14
|
作者
Elsersy, Wael F. [1 ]
Feizollah, Ali [1 ]
Anuar, Nor Badrul [1 ]
机构
[1] Univ Malaya, Dept Comp Syst & Technol, Fac Comp Sci & Informat Technol, Kuala Lumpur, Wilayah Perseku, Malaysia
关键词
Android malware; Android security; Evasion techniques; Machine learning; Obfuscation techniques; DEEP LEARNING-METHOD; HYBRID APPROACH; SYSTEM; FEATURES; CODE; SIGNATURE; FRAMEWORK; ANALYZER; ATTACKS; THREAT;
D O I
10.7717/peerj-cs.907
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The various application markets are facing an exponential growth of Android malware. Every day, thousands of new Android malware applications emerge. Android malware hackers adopt reverse engineering and repackage benign applications with their malicious code. Therefore, Android applications developers tend to use state-of-the-art obfuscation techniques to mitigate the risk of application plagiarism. The malware authors adopt the obfuscation and transformation techniques to defeat the anti-malware detections, which this paper refers to as evasions. Malware authors use obfuscation techniques to generate new malware variants from the same malicious code. The concern of encountering difficulties in malware reverse engineering motivates researchers to secure the source code of benign Android applications using evasion techniques. This study reviews the state-of-the-art evasion tools and techniques. The study criticizes the existing research gap of detection in the latest Android malware detection frameworks and challenges the classification performance against various evasion techniques. The study concludes the research gaps in evaluating the current Android malware detection framework robustness against state-of-the-art evasion techniques. The study concludes the recent Android malware detection-related issues and lessons learned which require researchers' attention in the future.
引用
收藏
页数:61
相关论文
共 50 条
  • [11] A Heuristic Approach for Detection of Obfuscated Malware
    Treadwell, Scott
    Zhou, Mian
    ISI: 2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS, 2009, : 291 - 299
  • [12] Author Correction: AndroDex: Android Dex Images of Obfuscated Malware
    Sana Aurangzeb
    Muhammad Aleem
    Muhammad Taimoor Khan
    George Loukas
    Georgia Sakellari
    Scientific Data, 11 (1)
  • [13] Android Malware Detection Using Deep Learning Methods
    Lukas, Robert
    Kolaczek, Grzegorz
    2021 IEEE 30TH INTERNATIONAL CONFERENCE ON ENABLING TECHNOLOGIES: INFRASTRUCTURE FOR COLLABORATIVE ENTERPRISES (WETICE 2021), 2021, : 119 - 124
  • [14] Formal Methods for Android Banking Malware Analysis and Detection
    Iadarola, Giacomo
    Martinelli, Fabio
    Mercaldo, Francesco
    Santone, Antonella
    2019 SIXTH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS: SYSTEMS, MANAGEMENT AND SECURITY (IOTSMS), 2019, : 331 - 336
  • [15] A study of feature selection methods for android malware detection
    Kshirsagar, Deepak
    Agrawal, Pooja
    JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES, 2022, 43 (08): : 2111 - 2120
  • [16] A Survey on Rise of Mobile Malware and Detection Methods
    Kalpana, S.
    Karthikeyan, S.
    2017 INTERNATIONAL CONFERENCE ON INNOVATIONS IN INFORMATION, EMBEDDED AND COMMUNICATION SYSTEMS (ICIIECS), 2017,
  • [17] AndroOBFS: Time-tagged Obfuscated Android Malware Dataset with Family Information
    Kumar, Saurabh
    Mishra, Debadatta
    Panda, Biswabandan
    Shukla, Sandeep Kumar
    2022 MINING SOFTWARE REPOSITORIES CONFERENCE (MSR 2022), 2022, : 454 - 458
  • [18] Comparison of Regression Methods in Permission Based Android Malware Detection
    Sahin, Durmus Ozkan
    Kural, Oguz Emre
    Akleylek, Sedat
    Kilic, Erdal
    2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2020,
  • [19] A brief survey of deep learning methods for android Malware detection
    Abdurraheem Joomye
    Mee Hong Ling
    Kok-Lim Alvin Yau
    International Journal of System Assurance Engineering and Management, 2025, 16 (2) : 711 - 733
  • [20] Android Malware Detection Methods Based on the Combination of Clustering and Classification
    Xiong, Zhi
    Guo, Ting
    Zhang, Qinkun
    Cheng, Yu
    Xu, Kai
    NETWORK AND SYSTEM SECURITY (NSS 2018), 2018, 11058 : 411 - 422