Issue |
E3S Web Conf.
Volume 371, 2023
International Scientific Conference “Fundamental and Applied Scientific Research in the Development of Agriculture in the Far East” (AFE-2022)
|
|
---|---|---|
Article Number | 01057 | |
Number of page(s) | 10 | |
Section | Smart Farming and Precision Agriculture | |
DOI | https://doi.org/10.1051/e3sconf/202337101057 | |
Published online | 28 February 2023 |
System for detecting of potentially dangerous communications of network users
Don State Technical University, 344002 Rostov-on-Don, Russia
* Corresponding author: revyelena@yandex.ru
This work is devoted to the processes of organizing internal information security at the enterprise. The scheme of a software tool for monitoring employee communication and detecting malicious messages using artificial neural network analysis and full-text dictionary search is proposed. A software package designed according to the described scheme has been developed. The scheme of interaction of the program components is considered: a keylogger, a keyboard input analyzer, a user interface, a server coordinating interaction. The schemes of interaction with the analyzer by means of the WebSocket protocol were shown. The interaction of the neural network and the dictionary helped to increase the percentage of accuracy of detecting a dangerous or suspicious message, for example, when the neural network does not consider the text dangerous, and the dictionary considers the opposite, then such text is considered dangerous and is shown to the administrator. Thus, the combination of neural network analysis and dictionary analysis makes it possible to detect more suspicious messages. The work of the software product and its advantages in comparison with other similar systems are demonstrated.
© The Authors, published by EDP Sciences, 2023
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.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.