| Autor: |
Margit Antal, Krisztian Buza, Norbert Fejer |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 9, Pp 124396-124408 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2021.3111098 |
| Popis: |
The growing interest in bot detection can be attributed to the fact that fraudulent actions performed by bots cause surprisingly high economical damage. State-of-the-art bots aim at mimicking as many as possible aspects of human behavior, ranging from response times and typing dynamics to human-like phrasing and mouse trajectories. In order to support research on bot detection, in this paper, we propose an approach to generate human-like mouse trajectories, called SapiAgent. To implement SapiAgent, we employ deep autoencoders and a novel training algorithm. We performed experiments on our publicly available SapiMouse dataset which contains human mouse trajectories collected from 120 subjects. The results show that SapiAgent is able to generate more realistic mouse trajectories compared with Bézier curves and conventional autoencoders. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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