RanABD: Web Page Randomization for Advanced Web-Bot Detection
Bibliographic record
Abstract
In the current digital landscape, cyberattacks have become increasingly sophisticated in their attempts to evade detection. One such example is the session-replay web bot attack, where hackers use previously recorded human mouse movements (i.e., sessions) to emulate human behavior on the target websites and apps. With the emergence of advanced AI, hackers are further expected to utilize these programs to generate carefully randomized session-replay bots that still exhibit human-like behavior but without replaying/repeating identical mouse trajectories, as was previously the case. Detecting such advanced bots in the traditionally designed web pages and sites is exceptionally hard if not impossible. In this paper, we propose RanABD, a novel defensive web page randomization technique designed to counter advanced web bots. RanABD performs randomized micro modifications in the alignment of select visual HTML elements and element attributes in the target web page, while causing minimal disturbances in the page's overall appearance and functionality. By doing so, the technique ensures that the distances between trajectories of genuine human-visitors, as well as trajectories of repeat visits by the same human user, are sufficiently separated in the Feature Space. For session-replay bot operators, the only way to bypass this defence is by increasing the degree of randomization in replay sessions, but this approach is likely to backfire as it inevitably results in outlier-like trajectories that are even easier to detect. According to our knowledge, this is the first research paper that explicitly addresses the issue of advanced session-replay bots as well as proposes a novel technique that can effectively detect these specific types of bots.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".