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• MULTIMODAL DATA FEATURE EXTRACTION METHOD FOR NETWORK ATTACK CLASSIFICATION

  A.V. Balyberdin

  6-16

  2025-07-24

  Abstract ▼

  An intrusion detection system (IDS) is an important component of corporate data network (CDN) protection. IDS analyzes network traffic and detects network attacks. Depending on the detection methods, IDS can be classified into the following types of systems: signature-based analysis systems, anomaly detection systems (ADS), and hybrid systems combining the aforementioned approaches. Recently, anomaly detection systems (IDS) have been actively developing. For anomaly detection systems, network attacks are anomalous behavior of network traffic consisting of a set of features or event attributes. Modern IDS are based on machine and deep learning methods, and therefore the detection of network attacks and anomalies is formulated as a classification and clustering problem. To solve these problems, methods for optimizing the feature space of network traffic are required. The aim of the work is to develop a feature extraction method based on a multimodal approach to representing network traffic data for classifying network attacks. The paper considers the analysis of relevant studies on feature extraction methods from various fields. The objective of the study is to improve classification efficiency using a multimodal representation of network traffic features. The result of the work is a method for extracting data features based on two modalities: a spectral representation of network traffic features and an image feature matrix. The novelty of the presented method lies in the application of the windowed Fourier transform method for network traffic events, followed by the calculation of spectral features for discrete signals, as well as the transformation of data features into an image matrix and its expansion to optimize the feature space using a convolutional neural network (CNN). Evaluation of the multimodal method showed that this method increased the classification accuracy for unbalanced classes of network attacks

• MACHINE LEARNING AND DEEP LEARNING MODELS FOR ELECTRONIC INFORMATION SECURITY IN MOBILE NETWORKS

  Aussi Rim Mohammed Hedhair, E.V. Zargaryan, Y.A. Zargaryan

  2022-08-09

  Abstract ▼

  Recent advances in wireless communication technologies have led to the creation of a huge
  amount of data that is transmitted everywhere. Most of this information is part of an extensive and
  publicly accessible network that connects various stationary and mobile devices around the world.
  The capabilities of electronic devices are also increasing day by day, which leads to more data
  generation and information exchange through networks. Similarly, with the increasing diversity
  and complexity of mobile network structures, the frequency of security breaches in it has increased.
  This hinders the introduction of intelligent mobile applications and services, as evidenced
  by the wide variety of platforms that provide data storage, data computing and application services to end users. In such scenarios, it becomes necessary to protect data and check their use in
  the network and applications, as well as check their incorrect use in order to protect private information.
  According to this study, a security model based on artificial intelligence should ensure
  the confidentiality, integrity and reliability of the system, its equipment and protocols that control
  the network, regardless of its creation, in order to manage such a complex network as a mobile
  one. The open difficulties that mobile networks still face, such as unauthorized network scanning,
  fraudulent links, etc., have been thoroughly studied in this article. This article also discusses several
  ML and DL technologies that can be used to create a secure environment, as well as many
  cybersecurity threats. It is necessary to address the need to develop new approaches to ensure a
  high level of electronic data security in mobile networks, since the possibilities for improving the
  security of mobile networks are limitless.