Prompted Contextual Vectors for Spear-Phishing Detection

G. Widmer and M. Kubat, “Learning in the presence of concept drift and hidden contexts,” Machine learning , vol. 23, pp. 69–101, 1996

1996

Earlier work this paper cites.

H. Shimodaira, “Improving predictive inference under covariate shift by weighting the log-likelihood function,” Journal of statistical planning and inference , vol. 90, no. 2, pp. 227–244, 2000

2000

Earlier work this paper cites.

R. B. Cialdini, “The science of persuasion,” Scientific American , vol. 284, no. 2, pp. 76–81, 2001

2001

Earlier work this paper cites.

B. Klimt and Y. Yang, “The enron corpus: A new dataset for email classification research,” in European conference on machine learning . Springer, 2004, pp. 217–226

2004

Earlier work this paper cites.

R. Alaiz-Rodríguez and N. Japkowicz, “Assessing the impact of changing environments on classifier performance,” in Advances in Artificial Intelligence: 21st Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2008 Windsor, Canada, May 28-30, 2008 Proceedings 21 . Springer, 2008, pp. 13–24

2008

Earlier work this paper cites.

L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research , vol. 9, no. 11, 2008

2008

Earlier work this paper cites.

L. Ma, B. Ofoghi, P. Watters, and S. Brown, “Detecting phishing emails using hybrid features,” in 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing . IEEE, 2009, pp. 493–497

2009

Earlier work this paper cites.

F. Toolan and J. Carthy, “Phishing detection using classifier ensembles,” in 2009 eCrime researchers summit . IEEE, 2009, pp. 1–9

2009

Earlier work this paper cites.

S. Bird, E. Klein, and E. Loper, Natural language processing with Python: analyzing text with the natural language toolkit . ” O’Reilly Media, Inc.”, 2009

2009

Earlier work this paper cites.

M. Khonji, Y. Iraqi, and A. Jones, “Mitigation of spear phishing attacks: A content-based authorship identification framework,” in 2011 International Conference for Internet Technology and Secured Transactions . IEEE, 2011, pp. 416–421

2011

Earlier work this paper cites.

I. R. A. Hamid and J. Abawajy, “Hybrid feature selection for phishing email detection,” in Algorithms and Architectures for Parallel Processing: 11th International Conference, ICA300 2011, Melbourne, Australia, October 24-26, 2011, Proceedings, Part II 11 . Springer, 2011, pp. 266–275

2011

Earlier work this paper cites.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al. , “Scikit-learn: Machine learning in python,” the Journal of machine Learning research , vol. 12, pp. 2825–2830, 2011

2011

Earlier work this paper cites.

J. G. Moreno-Torres, T. Raeder, R. Alaiz-Rodríguez, N. V. Chawla, and F. Herrera, “A unifying view on dataset shift in classification,” Pattern recognition , vol. 45, no. 1, pp. 521–530, 2012

2012

Earlier work this paper cites.

T. Almeida, J. M. Hidalgo, and T. Silva, “Towards sms spam filtering: Results under a new dataset,” International Journal of Information Security Science , vol. 2, no. 1, pp. 1–18, 2013

2013

Earlier work this paper cites.

G. Stringhini and O. Thonnard, “That ain’t you: detecting spearphishing emails before they are sent,” Citeseer , 2014

2014

Earlier work this paper cites.

A. A. Akinyelu, A. O. Adewumi et al. , “Classification of phishing email using random forest machine learning technique,” Journal of Applied Mathematics , vol. 2014, 2014

2014

Earlier work this paper cites.

N. Akbar, “Analysing persuasion principles in phishing emails,” Master’s thesis, University of Twente, 2014

2014

Earlier work this paper cites.

S. Smadi, N. Aslam, L. Zhang, R. Alasem, and M. A. Hossain, “Detection of phishing emails using data mining algorithms,” in 2015 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) . IEEE, 2015, pp. 1–8

2015

Earlier work this paper cites.

K. Allix, T. F. Bissyandé, J. Klein, and Y. Le Traon, “Are your training datasets yet relevant? an investigation into the importance of timeline in machine learning-based malware detection,” in International Symposium on Engineering Secure Software and Systems . Springer, 2015, pp. 51–67

2015

Earlier work this paper cites.

S. Duman, K. Kalkan-Cakmakci, M. Egele, W. Robertson, and E. Kirda, “Emailprofiler: Spearphishing filtering with header and stylometric features of emails,” in 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC) , vol. 1. IEEE, 2016, pp. 408–416

2016

Earlier work this paper cites.

G. I. Webb, R. Hyde, H. Cao, H. L. Nguyen, and F. Petitjean, “Characterizing concept drift,” Data Mining and Knowledge Discovery , vol. 30, no. 4, pp. 964–994, 2016

2016

Earlier work this paper cites.

M. Butavicius, K. Parsons, M. Pattinson, and A. McCormac, “Breaching the human firewall: Social engineering in phishing and spear-phishing emails,” arXiv preprint arXiv:1606.00887 , 2016

Original

2016

Earlier work this paper cites.

O. Zielinska, A. Welk, C. B. Mayhorn, and E. Murphy-Hill, “The persuasive phish: Examining the social psychological principles hidden in phishing emails,” in Proceedings of the Symposium and Bootcamp on the Science of Security , 2016, pp. 126–126

2016

Earlier work this paper cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017

2017

Earlier work this paper cites.

Anandita, D. P. Yadav, P. Paliwal, D. Kumar, and R. Tripathi, “A novel ensemble based identification of phishing e-mails,” in Proceedings of the 9th International Conference on Machine Learning and Computing , 2017, pp. 447–451

2017

Earlier work this paper cites.

H. Gascon, S. Ullrich, B. Stritter, and K. Rieck, “Reading between the lines: content-agnostic detection of spear-phishing emails,” in Research in Attacks, Intrusions, and Defenses: 21st International Symposium, RAID 2018, Heraklion, Crete, Greece, September 10-12, 2018, Proceedings 21 . Springer, 2018, pp. 69–91

2018

Earlier work this paper cites.

C. N. Gutierrez, T. Kim, R. Della Corte, J. Avery, D. Goldwasser, M. Cinque, and S. Bagchi, “Learning from the ones that got away: Detecting new forms of phishing attacks,” IEEE Transactions on Dependable and Secure Computing , vol. 15, no. 6, pp. 988–1001, 2018

2018

Cited alongside, same era.

O. Sagi and L. Rokach, “Ensemble learning: A survey,” Wiley interdisciplinary reviews: data mining and knowledge discovery , vol. 8, no. 4, p. e1249, 2018

2018

Cited alongside, same era.

A. Burns, M. E. Johnson, and D. D. Caputo, “Spear phishing in a barrel: Insights from a targeted phishing campaign,” Journal of Organizational Computing and Electronic Commerce , vol. 29, no. 1, pp. 24–39, 2019

2019

Cited alongside, same era.

A. Das, S. Baki, A. El Aassal, R. Verma, and A. Dunbar, “Sok: a comprehensive reexamination of phishing research from the security perspective,” IEEE Communications Surveys & Tutorials , vol. 22, no. 1, pp. 671–708, 2019

2019

Cited alongside, same era.

Checkpoint, “What is spear phishing?” 2024, https://www.checkpoint.com/tw/cyber-hub/threat-prevention/what-is-spear-phishing/

2024

Closest in time.

Forescout, “Threat profile: Credential harvesting attacks using spear-phishing,” 2024, https://www.forescout.com/resources/threat-profile-credential-harvesting-attacks-using-spear-phishing/

2024

Closest in time.

Zvelo, “The role of ai in social engineering,” 2024, https://zvelo.com/the-role-of-ai-in-social-engineering/

2024

Closest in time.

Y. Yao, J. Duan, K. Xu, Y. Cai, Z. Sun, and Y. Zhang, “A survey on large language model (llm) security and privacy: The good, the bad, and the ugly,” High-Confidence Computing , p. 100211, 2024

2024

Closest in time.

FBI, “Fbi warns of increasing threat of cyber criminals utilizing artificial intelligence,” 2024, https://www.fbi.gov/contact-us/field-offices/sanfrancisco/news/fbi-warns-of-increasing-threat-of-cyber-criminals-utilizing-artificial-intelligence