Detecting Malware for Android Platform: An SVM-based Approach

被引:35
|
作者
Li, Wenjia [1 ]
Ge, Jigang [1 ]
Dai, Guqian [1 ]
机构
[1] New York Inst Technol, Dept Comp Sci, New York, NY 10023 USA
来源
2015 IEEE 2ND INTERNATIONAL CONFERENCE ON CYBER SECURITY AND CLOUD COMPUTING (CSCLOUD) | 2015年
关键词
Android; malware; Support Vector Machine (SVM); TF-IDF;
D O I
10.1109/CSCloud.2015.50
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In recent years, Android has become one of the most popular mobile operating systems because of numerous mobile applications (apps) it provides. However, the malicious Android applications (malware) downloaded from third-party markets have significantly threatened users' security and privacy, and most of them remain undetected due to the lack of efficient and accurate malware detection techniques. In this paper, we study a malware detection scheme for Android platform using an SVM-based approach, which integrates both risky permission combinations and vulnerable API calls and use them as features in the SVM algorithm. To validate the performance of the proposed approach, extensive experiments have been conducted, which show that the proposed malware detection scheme is able to identify malicious Android applications effectively and efficiently.
引用
收藏
页码:464 / 469
页数:6
相关论文
共 50 条
  • [1] Linear SVM-Based Android Malware Detection
    Ham, Hyo-Sik
    Kim, Hwan-Hee
    Kim, Myung-Sup
    Choi, Mi-Jung
    FRONTIER AND INNOVATION IN FUTURE COMPUTING AND COMMUNICATIONS, 2014, 301 : 575 - 585
  • [2] Enhanced Android Malware Detection: An SVM-based Machine Learning Approach
    Han, Hyoil
    Lim, SeungJin
    Suh, Kyoungwon
    Park, Seonghyun
    Cho, Seong-je
    Park, Minkyu
    2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP 2020), 2020, : 75 - 81
  • [3] A SVM-based Malware Detection Mechanism for Android Devices
    Lu, Yung-Feng
    Kuo, Chin-Fu
    Chen, Hung-Yuan
    Chen, Chang-Wei
    Chou, Shih-Chun
    2018 INTERNATIONAL CONFERENCE ON SYSTEM SCIENCE AND ENGINEERING (ICSSE), 2018,
  • [4] AntiMalDroid: An Efficient SVM-Based Malware Detection Framework for Android
    Zhao, Min
    Ge, Fangbin
    Zhang, Tao
    Yuan, Zhijian
    INFORMATION COMPUTING AND APPLICATIONS, PT I, 2011, 243 : 158 - 166
  • [5] Linear SVM-Based Android Malware Detection for Reliable IoT Services
    Ham, Hyo-Sik
    Kim, Hwan-Hee
    Kim, Myung-Sup
    Choi, Mi-Jung
    JOURNAL OF APPLIED MATHEMATICS, 2014,
  • [6] A SVM-based approach for detecting tendon Injury
    Borzooei, Sahar
    Tournier, Pierre-Henri
    Dolean, Victorita
    Migliaccio, Claire
    2024 IEEE INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION AND INC/USNCURSI RADIO SCIENCE MEETING, AP-S/INC-USNC-URSI 2024, 2024, : 1511 - 1512
  • [7] Towards Deep Learning-Based Approach for Detecting Android Malware
    Booz, Jarrett
    McGiff, Josh
    Hatcher, William
    Yu, Wei
    Nguyen, James
    Lu, Chao
    INTERNATIONAL JOURNAL OF SOFTWARE INNOVATION, 2019, 7 (04) : 1 - 24
  • [8] SVM-Based Approach for Detecting Instantaneous Pain of Mice via Facial Expression
    Chen, Yu-Feng
    Lee, Kuan-Ru
    Wu, Chao-Cheng
    Chen, Chih-Cheng
    Lee, Cheng-Han
    2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2018, : 1681 - 1686
  • [9] A detection system of android malware based on SVM algorithm
    Huang, Lian-Fen
    Ye, Chao-Lin
    Feng, Chao
    Li, Han-Bo
    Zhang, Ying-Min
    Journal of Computers (Taiwan), 2019, 30 (04) : 151 - 158
  • [10] SVM-based ontology matching approach
    Lei Liu
    Feng Yang
    Peng Zhang
    Jing-Yi Wu
    Liang Hu
    International Journal of Automation and Computing, 2012, 9 (3) : 306 - 314