CIC-AB: Online ad blocker for browsers
Bibliographic record
Abstract
Online advertisements (ads) have taken over the web, nowedays most websites contain some sort of ads. While ads produce revenue for the server maintainer or to businesses, they have become intrusive and dangerous as ever. The ads use more bandwidth, show inappropriate content, and spread malware such as adware and ransomware. Although there are many products to block ads, also known as ad blockers, most depend on static filter lists that must be managed manually and frequently updated. When malicious advertisers can produce millions of new URLs within minutes, this is not the most effective method against ads. In this paper we propose our own ad blocker, CIC-AB, which uses machine learning techniques to detect new and unknown ads without needing to update a filter list. The proposed ad blocker has been developed as an extension for the common browsers (e.g. Firefox and Chrome). It classifies URLs, both HTTP and HTTPS, as: non-ad, normal-ad and malicious-ad. The analysis showed the average precision, recall and False Positive rate of CIC-AB for five classifiers namely; Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF) and Decision Tree (DT) is 97.16%, 94.96% and 3.38% respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.002 | 0.002 |
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".