You can SPIT, but you can't hide: Spammer identification in telephony networks
Bibliographic record
Abstract
Spam over Internet Telephony (SPIT) is a new form of spam delivered using the phone network. With the low cost of Internet telephony, SPIT has become an attractive alternative for spammers to carry out unsolicited marketing and phishing. SPIT is more intrusive than email spam as it demands immediate recipient attention. In this paper, we study characteristics of communications in a phone network with the objective of identifying “SPITters”. We collect and analyze the data from one of the largest phone providers in North America. First, we propose a new technique, Loose Tie Detection (LTD), to identify outliers based on social ties. Second, we introduce Enhanced Progressive Multi Grey-Leveling (EPMG), which identifies outliers based on call density and reciprocity. Finally, we propose SymRank, an adaptation of the PageRank algorithm that computes the reputation of subscribers based on both incoming and outgoing calls.We evaluate the three techniques and find that they compute an overlapping set of outliers. Our experiments reveal that LTD and SymRank - although seemingly independent approaches - closely match with regard to outliers, thus showing that our techniques are effective in identifying SPITters.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".