RETRACTED ARTICLE: A novel permission ranking system for android malware detection—the permission grader

被引:0
作者
Varna Priya Dharmalingam
Visalakshi Palanisamy
机构
[1] PSG College of Technology,Department of Electronics and Communication Engineering
来源
Journal of Ambient Intelligence and Humanized Computing | 2021年 / 12卷
关键词
Android; Malware detection; Permission; Static analysis; Deep learning;
D O I
暂无
中图分类号
学科分类号
摘要
Android is a profound, vanguard mobile operating system of the contemporary era. The quantity of mobile phone emptor dependent on Android platform is rising expeditiously, which expands its prominence everywhere throughout the world. The facts demonstrate that there are different sides to everything, with the remarkable achievement of the Android; attacks on Android operating system have been on the rise, as there are a lot of apps on the Internet that encompass malware. Malware is a segment of code composed with the aim of hurting a gadget or stealing the information in it. The proposed Permission Grading System performs static analysis of the apps to extract the requested permissions and to identify the permissions that are unique to malware and benign apps, by calculating the contribution of each of the permission. This helps identify the risk of the permissions that are requested by the apps. The results show an increase of about 20% in the detection of malware, with True Positive Rate values more than 0.85 and False Positive Rate values nearly fall below 0.03. These values are improved on using the familiar Term Frequency—Inverse Document Frequency weighting after the identification of unique permissions. This has led to achieve a True Positive Rate of more than 0.90 and False Positive Rate values were only 0.01.
引用
收藏
页码:5071 / 5081
页数:10
相关论文
共 50 条
  • [31] Permission-Based Feature Scaling Method for Lightweight Android Malware Detection
    Zhu, Dali
    Xi, Tong
    KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, KSEM 2019, PT I, 2019, 11775 : 714 - 725
  • [32] API Call and Permission Based Mobile Malware Detection (In English)
    Aysin, Ahmet Ilhan
    Sen, Sevil
    2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2015, : 2400 - 2403
  • [33] PFESG: Permission-based Android Malware Feature Extraction Algorithm
    Wang, Chengcheng
    Lan, Yuqing
    PROCEEDINGS OF 2017 VI INTERNATIONAL CONFERENCE ON NETWORK, COMMUNICATION AND COMPUTING (ICNCC 2017), 2017, : 106 - 109
  • [34] New Results on Permission Based Static Analysis for Android Malware
    Sahin, Durmus Ozkan
    Kural, Oguz Emre
    Akleylek, Sedat
    Kilic, Erdal
    2018 6TH INTERNATIONAL SYMPOSIUM ON DIGITAL FORENSIC AND SECURITY (ISDFS), 2018, : 340 - 343
  • [35] A formal approach for detection of security flaws in the android permission system
    Bagheri, Hamid
    Kang, Eunsuk
    Malek, Sam
    Jackson, Daniel
    FORMAL ASPECTS OF COMPUTING, 2018, 30 (05) : 525 - 544
  • [36] Android Malware Detection by Correlated Real Permission Couples Using FP Growth Algorithm and Neural Networks
    Banik, Abhinandan
    Singh, Jyoti Prakash
    IEEE ACCESS, 2023, 11 : 124996 - 125010
  • [37] HamDroid: permission-based harmful android anti-malware detection using neural networks
    Saeed Seraj
    Siavash Khodambashi
    Michalis Pavlidis
    Nikolaos Polatidis
    Neural Computing and Applications, 2022, 34 : 15165 - 15174
  • [38] HamDroid: permission-based harmful android anti-malware detection using neural networks
    Seraj, Saeed
    Khodambashi, Siavash
    Pavlidis, Michalis
    Polatidis, Nikolaos
    NEURAL COMPUTING & APPLICATIONS, 2022, 34 (18) : 15165 - 15174
  • [39] Permission-based Malware Detection Mechanisms for Smart Phones
    Su, Ming-Yang
    Chang, Wen-Chuan
    2014 INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN 2014), 2014, : 449 - 452
  • [40] Droid Permission Miner: Mining Prominent Permissions for Android Malware Analysis
    Aswini, A. M.
    Vinod, P.
    2014 FIFTH INTERNATIONAL CONFERENCE ON THE APPLICATIONS OF DIGITAL INFORMATION AND WEB TECHNOLOGIES (ICADIWT), 2014, : 81 - 86