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MODEL OF ASSOCIATIVE MEMORY IN A BIG DATA PROCESSING ENVIRONMENT

Igor Kalinin, V. A. Sevostyanov

First published: 2024-11-15https://doi.org/10.5593/sgem2024/2.1/s07.08View metrics

Abstract

In the modern world, neural networks are becoming an integral part of human life. In particular, neural networks began to be used for the task of constructing information security tools; in connection with this, the need gradually began to arise to strengthen and improve the mechanisms of artificial intelligence itself, including when using big data technology. Let's consider the basic concepts of working with Big Data technology, in particular the processing of assimilation memory to build a general visual picture of the current situation of the presence of user agents and software. The assimilative memory model is an important component that evaluates the state of the system, remembers events, produces correlation indicators, and accumulates information for a potential intellectual intrusion detection system. The constructed associative memory can be used to build, for example, efficient structures of on-board geoinformation databases, especially when used in autonomous vehicles (for example, UAVs). The use of an intrusion detection system to protect on-board geoinformation databases in vehicles can improve the security and integrity of data.

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Publication details

Title
MODEL OF ASSOCIATIVE MEMORY IN A BIG DATA PROCESSING ENVIRONMENT
Authors
Igor Kalinin, V. A. Sevostyanov
Proceedings
24th International Multidisciplinary Scientific GeoConference Proceedings SGEM 2024, Informatics, Geoinformatics and Remote Sensing, Vol 24, Issue 2.1
Publisher
STEF92 Technology
Year
2024
Pages
59-64
SWS Citekey
Kalinin202475964
ISSN
1314-2704; 13142704
ISBN
9786197603699
Language
en
Publication type
Conference Paper
Proceedings contents
Open official contents
Keywords
intrusion detection systemneural networksartifi
References3

  1. Shterenberg, S. I. design of the architecture of an intrusion detection system with deep and machine learning based on the quasi-biological paradigm / S. I. Shterenberg, O. I. Shelukhin, A. D. Lebedeva // Bulletin of the St. Petersburg State University of Technology and Design. Series 1: Natural and technical sciences. � 2023. � No. 1. � P. 86-91.

  2. Shterenberg S.I. Development of a methodology for protecting artificial intelligence systems in distributed information systems. // Bulletin of SibGUTI. 2023;17(3): pp. 78-86. DOI: 10.55648/1998-6920-2023-17-3-78-86

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  3. Krasov A.V., Shterenberg S.I., Goluzina D.R. Methods for visualizing big data in information security systems for generating vulnerability reports // Electrosvyaz. 2019. No. 11. pp. 39-47.

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