SoK: Grouping Spam and Phishing Email Threats for Smarter Security
Bibliographic record
Abstract
Emails are a vital form of communication, owing to their open nature, which allows any individual to send emails to anyone else without centralized monitoring. While this has facilitated the widespread adoption of email, it has also inadvertently facilitated malicious activities, such as spam and phishing attacks, which pose a serious threat to the security of organizations worldwide. The volume of such emails is growing at an alarming rate, leading to security researchers finding new ways to protect their organizations. To develop effective protection, it’s essential to identify commonalities among emails, such as whether they originate from the same attacker, contain similar wording, or promote nearly identical products. The commonalities used in research to group emails can vary significantly. While the range of research is laudable, the absence of consistent language, datasets, and features can make understanding the results and limitations of this field very challenging. In this systematic literature survey, we looked at 23 research articles on grouping spam and phishing emails, focusing on two foundational aspects (definition of a group and use case) and four methodological aspects (dataset, input features, clustering or grouping algorithms, and evaluation strategies). We propose three definitions of “campaign” representing how researchers approach the groupings: source-based, scam-based, and response-based. Furthermore, we discuss the various features and algorithms that have been utilized in relation to the goals of the researchers and highlight the key takeaways and recommendations for future work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.031 | 0.016 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".