Skip to main content

Generating Personalized Phishing Emails for Social Engineering Training Based on Neural Language Models

  • Conference paper
  • First Online:
Advances on Broad-Band Wireless Computing, Communication and Applications (BWCCA 2022)

Abstract

To prevent phishing attacks, social engineering training is a practical way by reinforcing the concepts of being aware of phishing emails. However, existing social engineering training relies on manual planning and artificially design, which suffers from scale and cost concerns. In this paper, we explore the idea of using natural language generation techniques for automatic social engineering training phishing mail generation. We present AI-Phishing, a novel phishing mail generation to facilitate personalized social engineering training planning. Users can utilize AI-Phishing to generate personalized phishing emails according to a given title and keywords.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Crestani, F., Lalmas, M., van Rijsbergen, C.J., Campbell, I.: “is this document relevant?... probably” a survey of probabilistic models in information retrieval. ACM Comput. Surv. (CSUR) 30(4), 528–552 (1998)

    Google Scholar 

  2. Das, S.D., Basak, A., Dutta, S.: A heuristic-driven ensemble framework for COVID-19 fake news detection. arXiv preprint arXiv:2101.03545 (2021)

  3. Lewis, M., et al.: Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)

  4. Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. In: Text summarization branches out, pp. 74–81 (2004)

    Google Scholar 

  5. Lin, T., et al.: Susceptibility to spear-phishing emails: effects of internet user demographics and email content. ACM Trans. Comput.-Hum. Interact. (TOCHI) 26(5), 1–28 (2019)

    Article  Google Scholar 

  6. Misra, R.: News category dataset (2018). https://doi.org/10.13140/RG.2.2.20331.18729

  7. Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002)

    Google Scholar 

  8. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)

    Google Scholar 

  9. Robertson, S.E., Walker, S., Jones, S., Hancock-Beaulieu, M.M., Gatford, M.: Okapi at TREC-3. Nist Spec. Publ. Sp 109, 109 (1995)

    Google Scholar 

  10. Salloum, S., Gaber, T., Vadera, S., Shaalan, K.: Phishing email detection using natural language processing techniques: a literature survey. Procedia Comput. Sci. 189, 19–28 (2021)

    Article  Google Scholar 

  11. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)

    Article  Google Scholar 

  12. Verma, A.: Fraud email dataset. kaggle (2018). https://www.kaggle.com/llabhishekll/fraud-email-dataset

  13. Wolf, T., et al.: Transformers: state-of-the-art natural language processing pp. 38–45 (2020)

    Google Scholar 

  14. Zellers, R., et al.: Defending against neural fake news. arXiv preprint arXiv:1905.12616 (2019)

  15. Zhu, Y., et al.: A benchmarking platform for text generation models. arxiv 2018. arXiv preprint arXiv:1802.01886 (2018)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fang-Yie Leu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Guo, SW., Chen, TC., Wang, HJ., Leu, FY., Fan, YC. (2023). Generating Personalized Phishing Emails for Social Engineering Training Based on Neural Language Models. In: Barolli, L. (eds) Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2022. Lecture Notes in Networks and Systems, vol 570. Springer, Cham. https://doi.org/10.1007/978-3-031-20029-8_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20029-8_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20028-1

  • Online ISBN: 978-3-031-20029-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics