Malwareless Web-Analytics Pollution (MWAP): A Very Simple Yet Invincible Attack
Bibliographic record
Abstract
Malwareless Web-analytics pollution (MWAP) attack is a type of cross-site attack that has been recently identified and discussed in [1]. The main aim of this attack is to distort the Web-site access logs of the victim/target company, and through that also distort this company's Web-based data analytics as well as its overall business performance. The key characteristics of the MWAP attack is that it is very easy to execute, as it does not involve the use of any malware, and it can be preformed simply by luring a set of random third-party users into visiting a specially crafted decoy Web-page. From that point on, through the sheer process of the decoy Web-page rendering, the browsers of the third-party users end up turning into temporary bots that generate a slew of legitimate looking HTTP request towards the Web-server of the victim site. The goal of our work was to investigate how effective three representative types of Web-analytics solutions (AWStats, Google Analytics and DataDome) would be in detecting several different variants of the MWAP attack. Our obtained experimental results show that, unfortunately, all of these solutions fail to detect some select variants of the MWAP attack, while one of the solutions fails to detect all of the examined MWAP variants. These results are very worrisome as the trend towards malwareless attack vectors is expected to accelerate in the coming years and we are likely to start seeing an increasing number of real-world incidents of the MWAP attack.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".