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Title: GUI-Squatting attack: automated generation of Android phishing apps
Authors: Chen, Sen
Fan, Lingling
Chen, Chunyang
Xue, Minhui
Liu, Yang
Xu, Lihua
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Chen, S., Fan, L., Chen, C., Xue, M., Liu, Y. & Xu, L. (2019). GUI-Squatting attack: automated generation of Android phishing apps. IEEE Transactions On Dependable and Secure Computing, 18(6), 2551-2568.
Project: NRF2018NCR-NSOE003-0001
Journal: IEEE Transactions on Dependable and Secure Computing
Abstract: Mobile phishing attacks, such as mimic mobile browser pages, masquerade as legitimate applications by leveraging repackaging or clone techniques, have caused varied yet significant security concerns. Consequently, detection techniques have been receiving increasing attention. However, many such detection methods are not well tested and may therefore still be vulnerable to new types of phishing attacks. In this article, we propose a new attacking technique, named GUI-Squatting attack, which can generate phishing apps (phapps) automatically and effectively on the Android platform. Our method adopts image processing and deep learning algorithms, to enable powerful and large-scale attacks. We observe that a successful phishing attack requires two conditions, page confusion and logic deception during attacks synthesis. We directly optimize these two conditions to create a practical attack. Our experimental results reveal that existing phishing defenses are less effective against such emergent attacks and may, therefore, stimulate more efficient detection techniques. To further demonstrate that our generated phapps can not only bypass existing detection techniques, but also deceive real users, we conduct a human study and successfully steal users' login information. The human study also shows that different response messages (e.g., 'Crash' and 'Server failed') after pressing the login button mislead users to regard our phapps as functionality problems instead of security threats. Extensive experiments reveal that such newly proposed attacks still remain mostly undetected, and are worth further exploration.
ISSN: 1545-5971
DOI: 10.1109/TDSC.2019.2956035
Schools: School of Computer Science and Engineering 
Rights: © 2019 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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