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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

• VERIFICATION OF DYNAMIC BIOMETRIC PARAMETERS OF A PERSONALITY BASED ON A PROBABLE NEURAL NETWORK

  Y.A. Bryuhomitsky

  2021-01-19

  Abstract ▼

  Biometric identity verification is used primarily for access to computer and mobile systems, as
  well as for remote (voice) verification. In fact, the most widespread systems are biometric verification
  systems based on a fixed passphrase, which are quite simple to implement, but very vulnerable to
  attacks of reproduction of a compromised short text. To eliminate this drawback, it is proposed to
  carry out identity verification using a text that is arbitrary in terms of volume, content and language
  (text-independent biometric verification). This paper proposes a generalized approach to solve the
  problem of identity verification by dynamic biometric parameters of different modality (keyboard
  writing, handwriting, voice). The presentation of dynamic biometrics signals is carried out by converting
  them into a sequences of information units, each of which contains the same number of counts
  of biometric signal of corresponding modality. The solution to this problem is carried out by monitoring
  the degree of concentration of closely located information units (clusters) at certain points of the
  multidimensional feature space. Such control is implemented on a probabilistic neural network thatstatistically evaluates the probability density of the distribution of information units in the corresponding
  clusters with the subsequent determination of the total probability density for the entire
  class of objects. The advantages of the proposed approach are: generalization of substantially different
  methods of text-independent identity verification by dynamic biometric parameters of different
  modality; the ability to make a verification decision for a fixed time of receipt of biometric data, determined
  by the size of the model used; the ability to set the verification accuracy by changing the
  dimension of the layer of probabilistic network samples. The disadvantage of the proposed approach
  is the need for software implementation of a large-scale neural network. However, this drawback is
  quickly leveled with an increase in the productivity of computer technology.