Analysis of Novel Mouse Dynamics Dataset with Repeat Sessions: Helpful Observations for Tackling Session-Replay Bot
Bibliographic record
Abstract
Session-replay bots are believed to be the latest and most sophisticated generation of web-bots, that are also very difficult to defend against. Combating session-replay bots is particularly challenging in online domains that get repeatedly visited by the same genuine human user(s), and possibly in the same/similar way - such as news, banking or gaming sites. Namely, in such domains, it is difficult to determine whether two look-alike sessions are produced by the same human user or these sessions are just bot-generated session replays. Unfortunately, to date, only a handful of research studies have looked at the problem of session-replay bots, with many related questions still waiting to be addressed. The main contributions of this paper are two-fold: 1) We introduce and provide to the public a novel real-world mouse dynamics dataset named ReMouse. ReMouse dataset is collected in a guided environment and, unlike other publicly available mouse dynamics dataset, it contains repeat-sessions generated by the same human user(s). As such, ReMouse dataset is first of its kind and is of particular relevance for studies on the development of effective defenses against session-replay bots. 2) Our own analysis of ReMouse dataset using statistical and advanced ML-based methods (including deep and unsupervised neural learning) shows that not only two different human users are highly unlikely to generate same/similar looking sessions when performing the same/similar online task, but even the (repeat) sessions generated by the same human user are likely to be sufficiently distinguishable from one another.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".