Detecting Malware with Similarity to Android applications

被引:0
|
作者
Park, Wonjoo [1 ]
Kim, Sun-joong [1 ]
Ryu, Won [1 ]
机构
[1] ETRI, Intelligent Convergence Media Res Dept, Daejeon, South Korea
关键词
Android malware; malware analysis; Smartphone security;
D O I
暂无
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 light of the rapid growth of smartphones, there are unrelenting malicious attacks on smartphones from voice phishing to mobile malwares. Especially, SMiShing malicious application has become a crucial threat on smartphone since it can be easily rampant via URLs embedded in SMS messages and emails. SMiShing attack installs the malicious application, it has been exploited by financial fraud and leak of private information stored on the smartphone. Our solution intercepts and gathers the malicious application and analyzing it instead of smartphone. It can block installing malicious application on smartphone and also analyze fast and accurately. Also, a number of malicious applications targeting Android operation system are similar to known malware and repackaged an existed malicious application. It presents a unique feature that the downloaded applications can be compared with accumulated malwares. In this paper, we propose the detection system for android malicious application using static analysis along with malicious feature similarity.
引用
收藏
页码:1249 / 1251
页数:3
相关论文
共 50 条
  • [1] A Hybrid Malware Detecting Scheme for Mobile Android Applications
    Liu, Yu
    Zhang, Yichi
    Li, Haibin
    Chen, Xu
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE), 2016,
  • [2] Infrastructure for Detecting Android Malware
    Delosieres, Laurent
    Garcia, David
    [J]. INFORMATION SCIENCES AND SYSTEMS 2013, 2013, 264 : 389 - 398
  • [3] Detecting and classifying method based on similarity matching of Android malware behavior with profile
    Jang, Jae-Wook
    Yun, Jaesung
    Mohaisen, Aziz
    Woo, Jiyoung
    Kim, Huy Kang
    [J]. SPRINGERPLUS, 2016, 5
  • [4] A Novel approach for detecting malware in Android applications using Deep learning
    Kaushik, Prashant
    Yadav, Pankaj K.
    [J]. 2018 ELEVENTH INTERNATIONAL CONFERENCE ON CONTEMPORARY COMPUTING (IC3), 2018, : 59 - 62
  • [5] An Effective Online Scheme for Detecting Android Malware
    Liang, Shuang
    Du, Xiaojiang
    Tan, Chiu C.
    Yu, Wei
    [J]. 2014 23RD INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND NETWORKS (ICCCN), 2014,
  • [6] DAMBA: Detecting Android Malware by ORGB Analysis
    Zhang, Weizhe
    Wang, Huanran
    He, Hui
    Liu, Peng
    [J]. IEEE TRANSACTIONS ON RELIABILITY, 2020, 69 (01) : 55 - 69
  • [7] Detecting Android Malware Using Clone Detection
    Jian Chen
    Manar H. Alalfi
    Thomas R. Dean
    Ying Zou
    [J]. Journal of Computer Science and Technology, 2015, 30 : 942 - 956
  • [8] Detecting Android Malware Using Clone Detection
    Chen, Jian
    Alalfi, Manar H.
    Dean, Thomas R.
    Zou, Ying
    [J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2015, 30 (05) : 942 - 956
  • [9] Detecting Android Malware with Intensive Feature Engineering
    Yang, Manzhi
    Wen, QiaoYan
    [J]. PROCEEDINGS OF 2016 IEEE 7TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS 2016), 2016, : 157 - 161
  • [10] Detecting Android Malware Using Bytecode Image
    Ding, Yuxin
    Wu, Rui
    Xue, Fuxing
    [J]. COGNITIVE COMPUTING (ICCC 2018), 2018, 10971 : 164 - 169