| Autors: Andreev, D. A., Petrakieva, S. K., Taralova I. Title: Reveal false names of accounts as a result of hackers attacks Keywords: security systems, chaotic generator, machine learning Abstract: The aim of the paper is to reveal false account names as a result of hackers' attacks. The probability that a given account is either false or actual is calculated using machine learning analysis methods. The suspected account will be used as a pattern, and by classification techniques, clusters will be formed with a respective probability that this name is false. The purpose of the investigation is to determine if there exists a trend that arises during the creation of new accounts, to detect false accounts, and to discriminate them from the real ones independently if two types of accounts are generated with the same speed. These security systems are applied in different areas where the security of the data in users’ accounts is required. For example, they can be used in online voting for balloting, in studying social opinion through inquiries, in protecting the information in different user accounts of a given system, etc. References Issue
Copyright IEEE Xplore |
Цитирания (Citation/s):
1. Julio Y. R., Mangones A. P., Tovio J. T., Hernández F. I., Aguilar J., Gomez S. K., Advancing Healthcare in Córdoba:Telemedicine as a Service (PGaaS) and Addressing OWASP Machine Learning Security Challenges, 2024, 50th Latin American Computing Conference, CLEI 2024, ISBN: 979-833154097-5, Bahia Blanca,12 - 16 August 2024, Code: 203210, DOI: 10.1109/CLEI64178.2024.10700515 https://www.scopus.com/record/display.uri?origin=citedby&eid=2-s2.0-85207827446&noHighlight=false&relpos=0 - 2024 - в издания, индексирани в Scopus или Web of Science
Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus
