Abstract / Description of output

Phishing is by far the most common and disruptive type of cyber-attack faced by most organizations. Phishing messages may share common attributes such as the same or similar subject lines, the same sending infrastructure, similar URLs with certain parts slightly varied, and so on. Attackers use such strategies to evade sophisticated email filters, increasing the difficulty for computing support teams to identify and block all incoming emails during a phishing attack. Limited work has been done on grouping human-reported phishing emails, based on the underlying scam, to help the computing support teams better defend organizations from phishing attacks. In this paper, we explore the feasibility of using unsupervised clustering techniques to group emails into scams that could ideally be addressed together. We use a combination of contextual and semantic features extracted from emails and perform a comparative study on three clustering algorithms with varying feature sets. We use a range of internal and external validation methods to evaluate the clustering results on real-world email datasets. Our results show that unsupervised clustering is a promising approach for scam identification and grouping, and analyzing reported phishing emails is an effective way of mitigating phishing attacks and utilizing the human perspective.
Original languageEnglish
Title of host publicationProceedings of the 15th ACM Workshop on Artificial Intelligence and Security (AISec 2022)
PublisherACM Association for Computing Machinery
Number of pages12
ISBN (Electronic)978-1-4503-9880-0
Publication statusPublished - 7 Nov 2022
Event15th ACM Workshop on Artificial Intelligence and Security - Los Angeles, United States
Duration: 11 Nov 202211 Nov 2022


Workshop15th ACM Workshop on Artificial Intelligence and Security
Country/TerritoryUnited States
CityLos Angeles
Internet address

Keywords / Materials (for Non-textual outputs)

  • phishing
  • security
  • Usable Security
  • Clustering
  • Context
  • Artificial Intelligence
  • , Human-Centered Artificial Intelligence


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