Phishing Kits Source Code Similarity Distribution: A Case Study
Bibliographic record
Abstract
Attackers (“phishers”) typically deploy source code in some host website to impersonate a brand or in general a situation in which a user is expected to provide some personal information of interest to phishers (e.g. credentials, credit card number). Phishing kits are ready-to-deploy sets of files that can be simply copied on a web server and used almost as they are. In this paper, we consider the static similarity analysis of the source code of 20871 phishing kits totalling over 182 million lines of PHP, Javascript and HTML code, that have been collected during phishing attacks and recovered by forensics teams. Reported experimental results show that as much as 90% of the analyzed kits share 90% or more of their source code with at least another kit. Differences are small, less than about 1000 programming words – identifiers, constants, strings and so on – in 40% of cases. A plausible lineage of phishing kits is presented by connecting together kits with the highest similarity. Obtained results show a very different reconstructed lineage for phishing kits when compared to a publicly available application such as Wordpress. Observed kits similarity distribution is consistent with the assumed hypothesis that kit propagation is often based on identical or near-identical copies at low cost changes. The proposed approach may help classifying new incoming phishing kits as “near-copy” or “intellectual leaps” from known and already encountered kits. This could facilitate the identification and classification of new kits as derived from older known kits.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".