Fetching the paper…
Reading the bibliography…
A vishing attack is a form of social engineering where attackers use phone calls to deceive individuals into disclosing sensitive information, such as personal data, financial information, or security credentials.
G. Ollmann, “The phishing guide,”
2004
Earlier work this paper cites.
S. Sheng, M. Holbrook, P. Kumaraguru, L. F. Cranor, and J. Downs, “Who falls for phish? a demographic analysis of phishing susceptibility and effectiveness of interventions,” in
2010
Earlier work this paper cites.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz
2011
Earlier work this paper cites.
E. O. Yeboah-Boateng and P. M. Amanor, “Phishing, smishing & vishing: an assessment of threats against mobile devices,”
2014
Earlier work this paper cites.
F. Mouton, L. Leenen, and H. Venter, “Social engineering attack detection model: Seadmv2,” in
2015
Earlier work this paper cites.
F. Mouton, L. Leenen, and H. S. Venter, “Social engineering attack examples, templates and scenarios,”
2016
Earlier work this paper cites.
S. Gupta, P. Gupta, M. Ahamad, and P. Kumaraguru, “Exploiting phone numbers and cross-application features in targeted mobile attacks,” in
2016
Earlier work this paper cites.
M. Sahin, M. Relieu, and A. Francillon, “Using chatbots against voice spam: Analyzing Lenny’s effectiveness,” in
2017
Earlier work this paper cites.
I. Ghafir, J. Saleem, M. Hammoudeh, H. Faour, V. Prenosil, S. Jaf, S. Jabbar, and T. Baker, “Security threats to critical infrastructure: the human factor,”
2018
Earlier work this paper cites.
L. Wan, Q. Wang, A. Papir, and I. L. Moreno, “Generalized end-to-end loss for speaker verification,” in
2018
Earlier work this paper cites.
H. Yuan, X. Chen, Y. Li, Z. Yang, and W. Liu, “Detecting phishing websites and targets based on urls and webpage links,” in
2018
Earlier work this paper cites.
H. Li, X. Xu, C. Liu, T. Ren, K. Wu, X. Cao, W. Zhang, Y. Yu, and D. Song, “A Machine Learning Approach to Prevent Malicious Calls over Telephony Networks,” in
2018
Earlier work this paper cites.
Z. A. Wen, Z. Lin, R. Chen, and E. Andersen, “What.hack: engaging anti-phishing training through a role-playing phishing simulation game,” in
2019
Earlier work this paper cites.
J. Petelka, Y. Zou, and F. Schaub, “Put your warning where your link is: Improving and evaluating email phishing warnings,” in
2019
Earlier work this paper cites.
K. Qian, Y. Zhang, S. Chang, X. Yang, and M. Hasegawa-Johnson, “AutoVC: Zero-shot voice style transfer with only autoencoder loss,” ser. Machine Learning Research, vol. 97. Long Beach, California, USA: PMLR, 09–15 Jun 2019, pp. 5210–5219
2019
Earlier work this paper cites.
S. Mishra and D. Soni, “Smishing detector: A security model to detect smishing through SMS content analysis and URL behavior analysis,”
2020
Earlier work this paper cites.
S. Prasad, E. Bouma-Sims, A. K. Mylappan, and B. Reaves, “Who’s calling? characterizing robocalls through audio and metadata analysis,” in
2020
Earlier work this paper cites.
K. S. Jones, M. E. Armstrong, M. K. Tornblad, and A. Siami Namin, “How social engineers use persuasion principles during vishing attacks,”
2021
Earlier work this paper cites.
G. Eren and The Coqui TTS Team, “Coqui TTS,” Jan. 2021. [Online]. Available:
2021
Earlier work this paper cites.
A. McDonald, C. Sugatan, T. Guberek, and F. Schaub, “The annoying, the disturbing, and the weird:challenges with phone numbers as identifiers and phone number recycling,” in
2021
Earlier work this paper cites.
K. Lee and A. Narayanan, “Security and privacy risks of number recycling at mobile carriers in the united states,” in
2021
Earlier work this paper cites.
S. Gopavaram, J. Dev, M. Grobler, D. Kim, S. Das, and L. J. Camp, “Cross-national study on phishing resilience,” in
2021
Cited alongside, same era.
T. Matsuura, A. A. Hasegawa, M. Akiyama, and T. Mori, “Careless participants are essential for our phishing study: Understanding the impact of screening methods,” in
2021
Cited alongside, same era.
E. Ulqinaku, H. Assal, A. Abdou, S. Chiasson, and S. Capkun, “Is Real-time Phishing Eliminated with FIDO? Social Engineering Downgrade Attacks against FIDO Protocols,” in
2021
Cited alongside, same era.
Y. Lin, R. Liu, D. M. Divakaran, J. Y. Ng, Q. Z. Chan, Y. Lu, Y. Si, F. Zhang, and J. S. Dong, “Phishpedia: A hybrid deep learning based approach to visually identify phishing webpages,” in
2021
Cited alongside, same era.
A. Fakieh and A. Akremi, “An Effective Blockchain-Based Defense Model for Organizations against Vishing Attacks,”
B. J. Jansen, S.-g. Jung, and J. Salminen, “Employing large language models in survey research,”
2023
Later among the works it cites.
C. Ebert and P. Louridas, “Generative ai for software practitioners,”
2023
Later among the works it cites.
“Chatgpt_dan,”
2023
Later among the works it cites.
E. Shayegani, M. A. A. Mamun, Y. Fu, P. Zaree, Y. Dong, and N. Abu-Ghazaleh, “Survey of vulnerabilities in large language models revealed by adversarial attacks,” 2023
2023
Later among the works it cites.
M. Russinovich, “Bluehat 2023: Mark russinovich keynote,” Microsoft Security Response Center (MSRC), Tel Aviv, Israel, 2023
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
E. Casanova, J. Weber, C. D. Shulby, A. C. Junior, E. Gölge, and M. A. Ponti, “YourTTS: Towards zero-shot multi-speaker TTS and zero-shot voice conversion for everyone,” in
2022
Cited alongside, same era.
W. Syafitri, Z. Shukur, U. Asma’Mokhtar, R. Sulaiman, and M. A. Ibrahim, “Social engineering attacks prevention: A systematic literature review,”
2022
Cited alongside, same era.
S. Y. Zheng and I. Becker, “Presenting suspicious details in user-facing e-mail headers does not improve phishing detection,” in
2022
Cited alongside, same era.
M. Nandakumar, R. Nachiappan, A. K. Sunil, J. C. Neves, H. P. Proença, and M. Sathiyanarayanan, “ScamBlk: A Voice Recognition-Based Natural Language Processing Approach for the Detection of Telecommunication Fraud,” in
2022
Cited alongside, same era.
L. Blue, K. Warren, H. Abdullah, C. Gibson, L. Vargas, J. O’Dell, K. Butler, and P. Traynor, “Who Are You (I Really Wanna Know)? Detecting Audio DeepFakes Through Vocal Tract Reconstruction,” in
2022
Cited alongside, same era.
V. Distler, “The influence of context on response to spear-phishing attacks: An in-situ deception study,” in
2023
Cited alongside, same era.
S. Mishra and D. Soni, “Dsmishsms-a system to detect smishing SMS,”
2023
Cited alongside, same era.
2023
Later among the works it cites.
S. Abdelnabi, K. Greshake, S. Mishra, C. Endres, T. Holz, and M. Fritz, “Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection,” in
2023
Later among the works it cites.
C. Stupp, “Fraudsters Used AI to Mimic CEO’s Voice in Unusual Cybercrime Case,” WSJ
2023
Later among the works it cites.
A. Kassis and U. Hengartner, “Breaking security-critical voice authentication,” in
2023
Later among the works it cites.
Z. Yu, S. Zhai, and N. Zhang, “AntiFake:using adversarial audio to prevent unauthorized speech synthesis,” in
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Hazell, “Large language models can be used to effectively scale spear phishing campaigns,”
2023
Later among the works it cites.
2023
Later among the works it cites.
Interpol,
2024
Closest in time.
Europol,
2024
Closest in time.
Oregon State University, Human Research Protection Program and IRB,
2024
Closest in time.
C. Jemine, “Real-time voice cloning,”
2024
Closest in time.
J. Vincent, Vice
2024
Closest in time.
A. Zhadan, Cybernews
2024
Closest in time.
J. Cox, Vice
2024
Closest in time.