Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220300114-7.doi: 10.11896/jsjkx.220300114

• Software & Interdiscipline • Previous Articles     Next Articles

Study on Android Fake Application Detection Method Based on Interface Similarity

FU Xiong, NIE Xiaohan, WANG Junchang   

  1. School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:FU Xiong,born in 1979,Ph.D,professor.His main research interests include cloud computing and distributed computing.
  • Supported by:
    National Natural Science Foundation of China(51977113) and Primary Research & Development Plan(Social Development) of Jiangsu Province(BE2017743).

PDF (PC)

337

Abstract: With the development of the Android,fake applications appear and become active on the Android platform.The popularity of obfuscation and other technologies makes it difficult for fake applications to be detected by traditional detection methods.In order to effectively resist the reinforcement technology,an Android fake application detection method(InfSimiDetec) based on interface similarity is proposed.Firstly,the layout information of the running interface is extracted by the automatic test tool.Next,the interface structural features are extracted based on the layout information.Then the interfaces with similar structural features are selected for interface similarity calculation.Finally,the application similarity calculation is carried out based on the ratio of similar interfaces.Experiments are carried out using a dataset containing multiple types of fake applications and compared with traditional detection methods.The results show that the precision rate of this method is 94.11% and the recall rate is 96.12%.Compared with traditional detection methods,this method shows better performance.

Key words: Android, Interface layout, Detection of fake application, Automation traversal of application, Feature extraction

CLC Number: 

  • TP393
[1]LI J L,WANG Y Z,LUO L G,et al.A Survey of Adversarial Attack Techniques for Android Malware Detection[J].Journal of Cyber Security,2021,6(4):28-43.
[2]WANG S Y,ZHANG Y S,CENG J R,et al.Overviewof Android Malware Detection Methods[J].Computer Applications and Software,2021,38(9):1-9.
[3]UTKARSH S,KULDEEP K,DEEPAKUMAR G.A Study ofCode Clone Detection Techniques in Software Systems[C]//Proceedings of the International Conference on Paradigms of Computing,Communication and Data Sciences.Springer,2021:347-359.
[4]HAMID A B,STAN J.A Data Mining Approach for Detecting Higher-Level Clones in Software[J].IEEE Transactions on Software Engineering,2009,35(4):497-514.
[5]CRUSSEL J,GIBLER C,HAO C.Attack of the Clones:Detecting Cloned Applications on Android Markets[C]//European Symposium on Research in Computer Secu-rity.Computer Security-ESORICS,2012:37-54.
[6]NIU H,YANG T,NIU S.Clone Analysis and Detection in Android Applications[C]//International Conference on Systems and Informatics(ICSAI).IEEE,2016:520-525.
[7]ZHOU W,ZHOU Y J,JIANG X X,et al.Detecting Repackaged Smartphone Applications in Third-party Android Marketplaces[C]//ACM Conference on Data and Application Security and Privacy.ACM,2012:317-326.
[8]MALISA L,KOSTIAINEN K,CAPKUN S.Detecting MobileApplication Spoofing Attacks by Leveraging User Visual Similarity Perception[C]//ACM Conference on Data and Application Secu-rity and Privacy.ACM,2017:289-300.
[9]SUN M,LI M,LUI J C S.DroidEagle:Seamless Detection of Visually Similar Android Apps[C]//ACM Conference on Secu-rity and Privacy in Wireless and Mobile Networks.ACM,2015:1-12.
[10]ZHAUNIAROVICH Y,LEZZA A L,GADYATSKAYA O.Evaluation of Resource-Based App Repackaging Detection in Android[C]//Nordic Conference on Secure IT Systems.2016:135-151.
[11]ZHU J,WU Z,ZHI G,et al.Appearance Similarity Evaluation for Android Applications[C]//International Conference on Advanced Computational Intelligence.IEEE,2015:323-328.
[12]LIU B,NATH S,GOVINDAN R,et al.DECAF:Detecting and Characterizing Ad Fraud in Mobile Apps[C]//USENIX Confe-rence on Networked Systems Design and Implementation(NSDI’14).USENIX Association,2014:57-70.
[13]KAICHEN.A List of Shared Libraries and AdLibraries Used in Android Apps[EB/OL].(2014-02-20)[2022-03-03].http://sites.psu.edu/kaichen/2014/02/20/a-list-of-shared-libraries-and-adlibraries-used-in-android-apps/.
[14]LUXEMBOURG UNIVERSITY.AndroZoo[EB/OL].(2019-03-21)[2022-03-03].https://androzoo.uni.lu/repackaging.
[15]ARP D,SPREITZENBARTH M,HUBNER M,et al.DREBIN:Effective and Explainable Detection of Android Malware in Your Pocket[C]//Network and Distributed System Security Sympo-sium.2014:23-38.
[16]LI L,BISSYANDE T F,KLEIN J.SimiDroid:Identifying and Explaining Similarities in Android Apps[C]//IEEE Trustcom/BigDataSE/ICESS.IEEE,2017:136-143.
[1] YUAN Jiangfeng, LI Haoxiang, YOU Wei, HUANG Jianjun, SHI Wenchang, LIANG Bin. Locating Third-party Library Functions in Obfuscated Applications [J]. Computer Science, 2023, 50(7): 293-301.
[2] LIU Hongyi, WANG Rui, WU Guanfeng, ZHANG Yang. Diesel Engine Fault Diagnosis Based on Kernel Robust Manifold Nonnegative Matrix Factorizationand Fusion Features [J]. Computer Science, 2023, 50(6A): 220400128-8.
[3] HUANG Xundi, PANG Xiongwen. Review of Intelligent Device Fault Diagnosis Based on Deep Learning [J]. Computer Science, 2023, 50(5): 93-102.
[4] YU Xingzhan, LU Tianliang, DU Yanhui, WANG Xirui, YANG Cheng. Android Malware Family Classification Method Based on Synthetic Image and Xception Improved Model [J]. Computer Science, 2023, 50(4): 351-358.
[5] WU Han, NIE Jiahao, ZHANG Zhaowei, HE Zhiwei, GAO Mingyu. Deep Learning-based Visual Multiple Object Tracking:A Review [J]. Computer Science, 2023, 50(4): 77-87.
[6] YANG Xiaoyu, LI Chao, CHEN Shunyao, LI Haoliang, YIN Guangqiang. Text-Image Cross-modal Retrieval Based on Transformer [J]. Computer Science, 2023, 50(4): 141-148.
[7] ZENG Zhi-xian, CAO Jian-jun, WENG Nian-feng, JIANG Guo-quan, XU Bin. Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism [J]. Computer Science, 2022, 49(7): 106-112.
[8] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[9] ZHANG Yuan, KANG Le, GONG Zhao-hui, ZHANG Zhi-hong. Related Transaction Behavior Detection in Futures Market Based on Bi-LSTM [J]. Computer Science, 2022, 49(7): 31-39.
[10] LIU Wei-ye, LU Hui-min, LI Yu-peng, MA Ning. Survey on Finger Vein Recognition Research [J]. Computer Science, 2022, 49(6A): 1-11.
[11] YAO Ye, ZHU Yi-an, QIAN Liang, JIA Yao, ZHANG Li-xiang, LIU Rui-liang. Android Malware Detection Method Based on Heterogeneous Model Fusion [J]. Computer Science, 2022, 49(6A): 508-515.
[12] WANG Yi, LI Zheng-hao, CHEN Xing. Recommendation of Android Application Services via User Scenarios [J]. Computer Science, 2022, 49(6A): 267-271.
[13] GAO Yuan-hao, LUO Xiao-qing, ZHANG Zhan-cheng. Infrared and Visible Image Fusion Based on Feature Separation [J]. Computer Science, 2022, 49(5): 58-63.
[14] ZUO Jie-ge, LIU Xiao-ming, CAI Bing. Outdoor Image Weather Recognition Based on Image Blocks and Feature Fusion [J]. Computer Science, 2022, 49(3): 197-203.
[15] REN Shou-peng, LI Jin, WANG Jing-ru, YUE Kun. Ensemble Regression Decision Trees-based lncRNA-disease Association Prediction [J]. Computer Science, 2022, 49(2): 265-271.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.