Threats to Online Advertising and Countermeasures
Bibliographic record
Abstract
Online advertising, also known as web advertising or Internet marketing, is the means and process of promoting products and services on the Internet, and it has been one of the important business models for the Internet. Due to its lucrative nature and its large scale of adoption, it has also been a target for malicious parties with various attack aims such as getting a cut of online advertising revenues, obtaining a user’s privacy, and spreading malware. Over the years, a great deal of research has been conducted on online advertising. Recently, the health of the online advertising ecosystem has become more of a concern for both advertisers and regular Internet users. Advertising budgets have been abused, and Internet users’ privacy and security have been infringed. In this article, we broadly study threats to online advertising and trace the root causes from a systems point of view. Existing threat mitigation strategies are also reviewed and analyzed. To protect online advertising, which has been an essential funding source of many free Internet services, several challenges still need to be addressed, including the need for transparency of the advertising ecosystem and software vulnerabilities on the client-side. To overcome these challenges, we conclude by brainstorming some innovative ideas on some potentially interesting and useful research directions.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".