Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 153 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 28 tok/s Pro
GPT-5 High 18 tok/s Pro
GPT-4o 100 tok/s Pro
Kimi K2 220 tok/s Pro
GPT OSS 120B 447 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

Smishing Dataset I: Phishing SMS Dataset from Smishtank.com (2402.18430v2)

Published 28 Feb 2024 in cs.CR

Abstract: While smishing (SMS Phishing) attacks have risen to become one of the most common types of social engineering attacks, there is a lack of relevant smishing datasets. One of the biggest challenges in the domain of smishing prevention is the availability of fresh smishing datasets. Additionally, as time persists, smishing campaigns are shut down and the crucial information related to the attack are lost. With the changing nature of smishing attacks, a consistent flow of new smishing examples is needed by both researchers and engineers to create effective defenses. In this paper, we present the community-sourced smishing datasets from the smishtank.com. It provides a wealth of information relevant to combating smishing attacks through the breakdown and analysis of smishing samples at the point of submission. In the contribution of our work, we provide a corpus of 1090 smishing samples that have been publicly submitted through the site. Each message includes information relating to the sender, message body, and any brands referenced in the message. Additionally, when a URL is found, we provide additional information on the domain, VirusTotal results, and a characterization of the URL. Through the open access of fresh smishing data, we empower academia and industries to create robust defenses against this evolving threat.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (23)
  1. Adesina S. Sodiya Adebukola S. Onashoga, Olusola O. Abayomi-Alli and David A. Ojo. An adaptive and collaborative server-side sms spam filtering scheme using artificial immune system. Information Security Journal: A Global Perspective, 24(4-6):133–145, 2015.
  2. Apwg — unifying the global response to cybercrime. https://apwg.org/.
  3. Automatic detection of smishing attacks by machine learning methods. In 2019 1st International Informatics and Software Engineering Conference (UBMYK), pages 1–3, 2019.
  4. Phishing url detection through top-level domain analysis: A descriptive approach. In Proceedings of the 6th International Conference on Information Systems Security and Privacy. SCITEPRESS - Science and Technology Publications, 2020.
  5. Dating phish: An analysis of the life cycles of phishing attacks and campaigns. ARES ’22, New York, NY, USA, 2022. Association for Computing Machinery.
  6. FCC. Robotext scams on the rise, 2022. Accessed on 12 10, 2023.
  7. An ensemble learning approach for sms spam detection. In 2023 9th International Conference on Web Research (ICWR), pages 125–128, 2023.
  8. IR-2022-167. Irs reports significant increase in texting scams; warns taxpayers to remain vigilant, 2022. Accessed: Dec 29, 2023.
  9. Dsmishsms-a system to detect smishing sms. Neural Computing & Applications, pages 1 – 18, 2021.
  10. On sms phishing tactics and infrastructure.
  11. Inside a phisher’s mind: Understanding the anti-phishing ecosystem through phishing kit analysis. In 2018 APWG Symposium on Electronic Crime Research (eCrime), pages 1–12, 2018.
  12. Join the fight against phishing phishtank. https://phishtank.org/.
  13. proofpoint. 2023 state of the phish report, 2023. Accessed on 12 10, 2023.
  14. Be the phisher – understanding users’ perception of malicious domains. In Proceedings of the 15th ACM Asia Conference on Computer and Communications Security, ASIA CCS ’20, page 263–276, New York, NY, USA, 2020. Association for Computing Machinery.
  15. Users really do respond to smishing. In Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy, CODASPY ’23, page 49–60, New York, NY, USA, 2023. Association for Computing Machinery.
  16. Characterizing the security of the sms ecosystem with public gateways. ACM Trans. Priv. Secur., 22(1), dec 2018.
  17. Smishing strategy dynamics and evolving botnet activities in japan. IEEE Access, 10:114869–114884, 2022.
  18. An empirical analysis of sms scam detection systems, 2022.
  19. Profiler: Distributed model to detect phishing. In 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS), pages 1336–1337, 2022.
  20. Gaurav Sood. virustotal: R Client for the virustotal API, 2021. R package version 0.2.2.
  21. Clues in tweets: Twitter-guided discovery and analysis of sms spam, 2022.
  22. Unveiling human factors and message attributes in a smishing study. arXiv preprint arXiv:2311.06911, 2023.
  23. Commercial anti-smishing tools and their comparative effectiveness against modern threats. In Proceedings of the 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks, pages 1–12, 2023.
Citations (4)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 1 tweet and received 10 likes.

Upgrade to Pro to view all of the tweets about this paper: