Social Engineering and Inter-Device Eavesdropping Using SCRCPY for Spoofing and Sniffing

Anne Ivena Wijaya, Yeni Rosa Damayanti, Dwi Indah Lestiani, Adinda Ayu Putri Sugiono, Sherissa Callista Huanggino, Demas Muhammad Rijal, Renny Sari Dewi

Abstract


The rapid development of technology opens up new avenues for cybercriminals to carry out acts of social engineering and eavesdropping between devices. Social engineering refers to manipulation, influence, or deception used to gain control of a computer system. This technique is utilized by cybercriminals to steal sensitive information from their victims. This research focuses on analyzing social engineering and eavesdropping techniques between devices using SCRCPY, an Android screen control tool that allows remote control of Android devices from a computer. SCRCPY presents new opportunities for cybercriminals to launch spoofing and sniffing attacks, two common techniques used in social engineering attacks. This research explores various eavesdropping scenarios, analyzing interactions between devices and users, as well as security system responses to suspicious activity. The results show that SCRCPY can be used for various social engineering and eavesdropping attacks. Cybercriminals can use SCRCPY to gain control of a victim's Android device through the perpetrator's desktop computer. These findings indicate that social engineering attacks and eavesdropping between devices are becoming increasingly sophisticated and difficult to detect. Therefore, users need to increase their awareness of these risks and take steps to protect themselves.
Keywords: Social engineering, SCRCPY, spoofing, sniffing, device security.

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References


N. Y. Conteh and P. J. Schmick, “Cybersecurity Risks, Vulnerabilities, and Countermeasures to Prevent Social Engineering Attacks,” in Ethical Hacking Techniques and Countermeasures for Cybercrime Prevention, N. Conteh, Ed., IGI Global, 2021, pp. 19–31. doi: 10.4018/978-1-7998-6504-9.ch002.

Z. Wang, L. Sun, and H. Zhu, “Defining Social Engineering in Cybersecurity,” IEEE Access, vol. 8, pp. 85094–85115, 2020, doi: 10.1109/ACCESS.2020.2992807.

D. GlĂVan, C. RĂCuciu, R. Moinescu, and S. Eftimie, “Sniffing attacks on computer networks,” Scientific Bulletin of Naval Academy, vol. 23, no. 1, pp. 202–207, 2020, doi: 10.21279/1454-864X-20-I1-027.

N. Arumugam, “A Novel Method for Detecting and Preventing IP Spoofing Attack in Data Network,” Aug. 2018.

R. Tuli, “Packet Sniffing and Sniffing Detection,” International Journal of Innovations in Engineering and Technology, vol. 16, no. 1, pp. 22–32, Apr. 2020, doi: 10.21172/ijiet.161.04.

J. R. van der Merwe, X. Zubizarreta, I. Lukčin, A. Rügamer, and W. Felber, “Classification of Spoofing Attack Types,” in 2018 European Navigation Conference (ENC), 2018, pp. 91–99. doi: 10.1109/EURONAV.2018.8433227.

F. Salahdine and N. Kaabouch, “Social Engineering Attacks: A Survey,” Future Internet, vol. 11, p. 89, 2019, doi: 10.3390/FI11040089.

U. N. and S. T. and H. S. and R. S. Mashtalyar Nikol and Ntaganzwa, “Social Engineering Attacks: Recent Advances and Challenges,” in HCI for Cybersecurity, Privacy and Trust, A. Moallem, Ed., Cham: Springer International Publishing, 2021, pp. 417–431.

A. Koyun, “Social Engineering Attacks,” 2020, doi: 10.1002/9781119672357.ch12.

D. Xu and H. Zhu, “Proactive Eavesdropping for Wireless Information Surveillance Under Suspicious Communication Quality-of-Service Constraint,” IEEE Trans Wirel Commun, vol. PP, p. 1, 2022, doi: 10.1109/TWC.2021.3138446.

J. H. Anajemba, Y. Tang, C. Iwendi, A. Ohwoekevwo, G. Srivastava, and O. Jo, “Realizing Efficient Security and Privacy in IoT Networks,” Sensors (Basel), vol. 20, 2020, doi: 10.3390/s20092609.

H. Lu, X. Helu, C. Jin, Y. Sun, M. Zhang, and Z. Tian, “Salaxy: Enabling USB Debugging Mode Automatically to Control Android Devices,” IEEE Access, vol. 7, pp. 178321–178330, 2019, doi: 10.1109/ACCESS.2019.2958837.

J. Amarante and J. P. Barros, “Exploring USB connection vulnerabilities on android devices breaches using the android debug bridge,” in ICETE 2017 - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications, SciTePress, 2017, pp. 572–577. doi: 10.5220/0006475905720577.

K. Opasiak and W. Mazurczyk, “(In)Secure Android Debugging: Security analysis and lessons learned,” Comput Secur, vol. 82, pp. 80–98, 2019, doi: https://doi.org/10.1016/j.cose.2018.12.010.

H. Hasanah, “Teknik-Teknik Obsevasi (Sebuah Alternatif Metode Pengumpulan Data Kualitatif Ilmu-ilmu Sosial),” At-Taqaddum, vol. 8, no. 1, pp. 21–46, 2017, doi: 10.21580/at.v8i1.1163.

R. S. Dewi and Y. S. Dharmawan, “A Proposed Model for Embedding Risk Proportion in Software Development Effort Estimation,” Procedia Comput Sci, vol. 234, pp. 1777–1784, 2024, doi: https://doi.org/10.1016/j.procs.2024.03.185.




DOI: https://doi.org/10.32520/stmsi.v13i5.4223

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