Comparing Macro and Micro Approaches for Detecting Phishing Where It Spreads
Bibliographic record
Abstract
Phishing attacks have evolved, increasingly leveraging legitimate platforms to bypass traditional domain and URL-based detection methods. Existing proactive approaches such as monitoring Domain Name System (DNS) anomalies and Certificate Transparency (CT) logs have become less effective, as attackers now exploit trusted websites and services. To address this challenge, we present TelePhish, a dataset of annotated Telegram messages collected from public groups in high-risk categories such as Cryptocurrency, Games, and Darknet. Unlike prior works, TelePhish emphasizes the propagational behavior of phishing by capturing Telegram group interactions, message patterns, and user engagement, rather than relying on easily manipulated textual or URL features. Using this dataset, we investigate how modeling granularity impacts phishing detection performance by adopting a macro-to-micro approach. We start from a general model that captures broad phishing patterns across Telegram, then progressively narrow to more specialized, category-aware models. We evaluate four modeling strategies: (i) a general model trained on all data, (ii) general-cluster models that segment the dataset via clustering and then train models, (iii) category models trained on each category, and (iv) categorycluster models that discover sub-patterns within each category. We show that localized models can outperform general models in capturing category-specific patterns. These findings provide guidelines for designing detection models that can be tailored to either capture general phishing patterns across the entire platform or address the unique characteristics of individual categories.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".