SHS Web Conf.
Volume 116, 202110th Annual International Conference “Schumpeterian Readings” (ICSR 2021)
|Number of page(s)||4|
|Published online||30 July 2021|
Business process model with “RandomForest” algorithm
1 Siberian Federal University, 79, Svobodny Av., Krasnoyarsk, 660041, Russian Federation
2 Reshetnev Siberian State University of Science and Technology, 31, Krasnoyarsky Rabochy Av., 660037 Krasnoyarsk, Russian Federation
3 Krasnoyarsk State Agrarian University, 2 Kirenskogo, Krasnoyarsk, 660074, Russian Federation
* Corresponding author: email@example.com
Currently, the areas of application of machine learning are multifaceted: artificial intelligence, financial applications, bioinformatics, intellectual games, speech and text recognition, computer language processing, medical diagnostics, technical diagnostics, text search and rubrication. Machine learning is an area of scientific knowledge associated with learning-capable algorithms. The use of machine learning methods is explained by the fact that for most intelligent complex tasks (for example, speech recognition, etc.) it is almost impossible to develop an obvious algorithm for their solution. However, you can teach a computer to learn how to solve such problems. In our article, we propose a model based on the machine learning algorithm “RandomForest”, which allows one to recognize bots by HTTP sessions. The chosen algorithm provides many advantages: non-iterative learning; high quality of the resulting models (comparable to neural networks and ensembles of neural networks); a small number of adjustable parameters. It works well with missing data (retains good accuracy); internal assessment of the generalizability of the model; able to work with raw data without preprocessing. The algorithm was trained on a dataset of more than 5000 sessions. The prospects of this direction are obvious, since robotic traffic accounts for more than 40% of the total Internet traffic.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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