ReMouse Dataset: On the Efficacy of Measuring the Similarity of Human-Generated Trajectories for the Detection of Session-Replay Bots

Abstract

Session-replay bots are believed to be the latest and most sophisticated generation of web bots, and they are also very difficult to defend against. Combating session-replay bots is particularly challenging in online domains that are repeatedly visited by the same genuine human user(s) in the same or similar ways—such as news, banking or gaming sites. In such domains, it is difficult to determine whether two look-alike sessions are produced by the same human user or if 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. The ReMouse dataset is collected in a guided environment, and, unlike other publicly available mouse dynamics datasets, it contains repeat sessions generated by the same human user(s). As such, the ReMouse dataset is the 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 two different human users cannot generate the same or similar-looking sessions when performing the same or a similar online task; furthermore, even the (repeat) sessions generated by the same human user are sufficiently distinguishable from one another.

Keywords

• behavioral biometrics
• mouse dynamics
• feature learning
• convolutional neural network
• clustering algorithms