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Ventinel : Automated Detection of Android Vishing Apps Using Optical Character Recognition

Author

Listed:
  • Daegyeom Kim

    (Graduate School of Software, Soongsil University, Seoul 06978, Republic of Korea)

  • Sehwan O

    (Graduate School of Software, Soongsil University, Seoul 06978, Republic of Korea)

  • Younghoon Ban

    (Graduate School of Software, Soongsil University, Seoul 06978, Republic of Korea)

  • Jungsoo Park

    (Department of ICT Convergence Science, Kangnam University, Yongin 16979, Republic of Korea)

  • Kyungho Joo

    (Graduate School of Software, Soongsil University, Seoul 06978, Republic of Korea)

  • Haehyun Cho

    (Graduate School of Software, Soongsil University, Seoul 06978, Republic of Korea)

Abstract

Vishing, a blend of “voice” and “phishing”, has evolved to include techniques like Call Redirection and Display Overlay Attacks, causing significant financial losses. Existing research has largely focused on user behavior and awareness, leaving gaps in addressing attacks originating from vishing applications. In this work, we present Ventinel , an Android-based defense system designed to detect these attacks without requiring OS modifications. Ventinel employs Optical Character Recognition (OCR) to compare phone numbers during calls, effectively preventing Call Redirection and Display Overlay Attacks. Additionally, it safeguards against Duplicated Contacts Attacks by cross-referencing call logs and SMS records. Ventinel achieves 100% detection accuracy, surpassing commercial applications, and operates with minimal data collection to ensure user privacy. We also describe malicious API behavior and demonstrate that the same behavior is possible for API levels 29 and higher. Furthermore, we analyze the limitations of existing solutions and propose new attack and defense strategies.

Suggested Citation

  • Daegyeom Kim & Sehwan O & Younghoon Ban & Jungsoo Park & Kyungho Joo & Haehyun Cho, 2025. "Ventinel : Automated Detection of Android Vishing Apps Using Optical Character Recognition," Future Internet, MDPI, vol. 17(1), pages 1-19, January.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:1:p:24-:d:1561995
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