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2022-10-20
Anashkin, Yegor V., Zhukova, Marina N..  2021.  About the System of Profiling User Actions Based on the Behavior Model. 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). :191—195.
The paper considers the issue of increasing the level of trust to the user of the information system by applying profiling actions. The authors have developed the model of user behavior, which allows to identify the user by his actions in the operating system. The model uses a user's characteristic metric instead of binary identification. The user's characteristic demonstrates the degree to which the current actions of the user corresponding to the user's behavior model. To calculate the user's characteristic, several formulas have been proposed. The authors propose to implement the developed behavior model into the access control model. For this purpose, the authors create the prototype of the user action profiling system for Windows family operating systems. This system should control access to protected resources by analyzing user behavior. The authors performed a series of tests with this system. This allowed to evaluate the accuracy of the system based on the proposed behavior model. Test results showed the type I errors. Therefore, the authors invented and described a polymodel approach to profiling actions. Potentially, the polymodel approach should solve the problem of the accuracy of the user action profiling system.
2017-05-18
Zhou, Pengyuan, Kangasharju, Jussi.  2016.  Profiling and Grouping Users to Edge Resources According to User Interest Similarity. Proceedings of the 2016 ACM Workshop on Cloud-Assisted Networking. :43–48.

Cloud computing provides a shared pool of resources for large-scale distributed applications. Recent trends such as fog computing and edge computing spread the workload of clouds closer towards the edge of the network and the users. Exploiting the edge resources efficiently requires managing the resources and directing user traffic to the correct edge servers. In this paper we propose to profile and group users according to their interest profiles. We consider edge caching as an example and through our evaluation show the potential benefits of directing users from the same group to the same caches. We investigate a range of workloads and parameters and the same conclusions apply. Our results highlight the importance of grouping users and demonstrate the potential benefits of this approach.