## 7 Conclusions and Future Work  
In this work, we presented an in-depth analysis of our novel real-world mouse dynamics dataset, the ReMouse dataset. We began by reviewing the literature that investigated mouse dynamics in the context of user authentication and bot detection. We also provided a summary of several publicly available mouse dynamics datasets. We then analyzed the ReMouse dataset using statistical and advanced ML-based methods, including deep and unsupervised neural learning. In the first stage of the preliminary analysis using statistical methods, we focused on analyzing the sessions generated by each individual user in isolation from other users. Second, the focus was on the pairwise analysis of sessions generated by different users. Based on the preliminary analysis of our novel ReMouse dataset, we concluded that although sessions generated by genuine human users are relatively similar to each other, there always exist some minimum distinguishable differences between them. We showed that sessions whose 'difference' from each other is below the determined threshold should potentially be flagged as'replay' sessions generated by session-replay bots. Considering the fact that the generalization value of the results obtained using manual feature selection is often questionable, we then investigated the (dis)similarities between comparable (same-order number) web sessions generated by different users by means of advanced machine learning techniques. The results further supported our earlier hypothesis that session trajectories generated by different users while completing the same online task are sufficiently distinguishable from each other. According to our knowledge, the ReMouse dataset is the first publicly available mouse dynamics dataset containing repeat sessions generated by the same human user(s). As such, this dataset can be a very valuable resource for research studies that aim to improve our understanding of (human) user behavior during repetitive interactions with the same web-site, with the ultimate goal of developing effective techniques for the detection of, and defense against, sessions-replay bots. We believe that the ReMouse dataset contains enough statistical data to facilitate unbiased and high-quality research in the above-mentioned research areas. However, we also would like to point out a few possible, though minor, limitations of our dataset and work. One potential limitation of our dataset/work can be related to the platform we used to collect the data, MTurk. Although MTurk workers are generally pretty diverse when it comes to their place of residence or profession, they tend to be less diverse in terms of their age, education, computer-use proficiency, etc. [56]. This can complicate how data can be interpreted, affecting the reliable and validity of our conclusions, as well as the generalizability of such results. Nevertheless, more importantly, this study is the first of its kind, so it effectively demonstrates the importance of filling the literature gaps, highlighting the need for further development in the area of our study. This work aims to bring more attention to the problems/threats posed by session-replay web bots, which carry out the most advanced types of malicious web bot attacks. Therefore, we invite other researchers to work alongside us. We made some progress in providing the data and tools and hope to facilitate further studies by other researchers. For future work, we plan to extend our image-based ML analysis of the ReMouse dataset by considering other aspects of mouse dynamics rather than just trajectory (e.g., by additionally embedding the information on time, mouse velocity and click events into the image representation of a user session). We are also currently working on incorporating the malicious sessions generated by actual session-replay bots into the ReMouse dataset.