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• FORMATION OF PARAMETERS OF INFORMATION SOURCES FOR NEURO-LINGUISTIC TEXT IDENTIFICATION

  К.Y. Rumyantsev , V. V. Kotenko , L.К. Khadzhieva

  173-188

  2026-07-07

  Abstract ▼

  This paper explores a method of neurolinguistic text identification aimed at analyzing and verifying information sources, including texts generated by artificial intelligence systems. Three versions of the information states of the text of Luo Guanzhong's Romance of the Three Kingdoms are analyzed: the original text and the text generated by the Gemini and GPT artificial intelligence systems. The study aims to formulate and substantiate parameters for use as identification factors in the generated text, as well as to create 3D images of neurolinguistic textual identification of information sources. The specialized software package "Neurolinguistic Text Identification Analyzer" is used, processing text data based on horizontal and vertical scanning of neurolinguistic information frames. As a result, information spectra, quantitative characteristics (information capacity, entropy, redundancy), and 3D neurolinguistic information images of neurolinguistic frames of textual information of the Chinese work are formed. A comparison of the identity levels of neurolinguistic 3D informational images of the textual information source and neurolinguistic information frames shows that the highest level of identity is observed when comparing the texts of neurolinguistic information frames with the original text, while the lowest level of identity is observed when comparing the original text with the text generated using neural networks. The obtained results demonstrate significant differences between the parameters of the neurolinguistic information frames of the original text and the parameters of the text generated by neural networks, both in terms of quantitative text characteristics and the characteristics of the neurolinguistic 3D informational images. It was found that the neurolinguistic 3D informational images of texts generated by neural networks have a smoother visual representation structure and an excellent color distribution compared to the neurolinguistic 3D images of the original text. The practical significance of this study lies in the application of an approach that allows for the identification of generated text and the verification of information sources. The obtained results open up prospects for further work and the possibility of creating programs capable of detecting the presence of text generation

• APPLICATION OF A HYBRID NEURAL NETWORK AE-LSTM FOR ANOMALIES DETECTION IN CONTAINER SYSTEMS

  I.V. Kotenko, М.V. Melnik

  2024-11-10

  Abstract ▼

  The popularity of container systems attracts the attention of many researchers in the field of information
  technology. Containerization technology allows to reduce the cost of computing resources when
  deploying and supporting complex infrastructure solutions. Ensuring the security of container systems and
  containerization in general, as well as the use of smart attacks based on artificial intelligence by malefactors,
  is a serious problem on the way to the safe and stable operation of container systems. This article
  proposes an approach for detecting not only previously unknown individual anomalous processes, but also
  anomalous process sequences in container systems. The proposed approach and its implementation based
  on the Docker platform are based on tracing system calls, constructing histograms of running processes,
  and using the AE-LSTM neural network. The process of constructing histograms is based on accounting of
  the number of executed system calls for each individual process. This solution provides the ability not only
  to accurately identify any process in the system, but also to effectively detect anomalous process sequences
  with a high degree of accuracy. The generated sequences are used as input data for the neural network.
  After completing the training process, the neural network acquires the ability to detect anomalous sequences
  by comparing a given threshold of reconstruction error with the actual error level of the input
  data vector. When the neural network encounters a new input data vector, it calculates the reconstruction
  error level - the difference between the expected and actual value. If this error exceeds a predetermined threshold, the system signals the presence of an anomaly in the sequence. Experiments show that the proposed
  approach demonstrates high accuracy in detecting anomalous processes with a low level of false
  positive detection results. Such results confirm the effectiveness of the proposed approach. Also, the computational
  costs of training the neural network model are quite low. This allows using less powerful hardware
  without significant performance losses. Such a solution can be trained and implemented in a new
  infrastructure in a fairly short time

• NEUROLINGUISTIC INFORMATION IDENTIFICATION OF INTELLIGENT SYSTEMS

  L.К. Khadzhieva, V.V. Kotenko, К.Y. Rumyantsev

  2024-08-12

  Abstract ▼

  The results of studies of the possibilities of using language and its components (text and speech) as
  factors of neurolinguistic identification and authentication of intelligent systems (IS) of native speakers of
  Russian and Chechen languages are presented. To achieve the research goals, an approach based on
  information virtualization was used. It is proposed to use one of the ways to solve the problems of increasing
  the efficiency of identification and authentication, which is the use of the factor of linguistic
  neurolinguistic text identification and authentication. Research shows, firstly, that when a language
  changes, in the case of using an intelligent system as a speaker of several languages, there is a change in
  the parameters of neurolinguistic identification, and secondly, that if all intelligent systems are native
  speakers of the same language, then when moving from one intellectual system to the other is a change in
  the parameters of neurolinguistic identification. Thus, the study determined that the language of an intelligent
  system can be used as an identification and authentication factor. IP speakers who are native speakers
  of both Chechen and Russian languages have been studied. At the first stage, ten IPs were studied as
  native speakers of the Russian language, and at the second stage, the same ten IPs were studied, but as
  native speakers of the Chechen language. The results of the dependence of the main parameters, as well as
  the dependence of the derived parameters of neurolinguistic text identification of intellectual systems of
  native speakers of Russian and Chechen languages are presented. The results obtained open up a fundamentally
  new opportunity for research in the direction of neurolinguistic text identification and authentication.
  Research in this direction is of scientific and practical interest, both for the case of identifying an
  intellectual system of native speakers of one language, and for the case when one intellectual system is a
  native speaker.