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• METHOD FOR SEARCHING SEQUENTIAL PATTERNS OF USER'S BEHAVIOR ON THE INTERNET

  V.V. Kureychik, V. V. Bova, Y.A. Kravchenko

  2020-11-22

  Abstract ▼

  One of the important tasks of data mining is to isolate patterns and detect related events in
  sequential data based on the analysis of sequential patterns. The article examines the possibility of
  using sequential patterns to analyze the events of search and cognitive activity of users when interacting
  with Internet resources of an open information and educational environment. Searching
  for sequential patterns is a complex computational task whose goal is to retrieve all frequent sequences
  representing potential relationships within elements from a transactional database of
  sequences of search activity events with a given minimum support. To solve it, the article proposes
  a method for searching for patterns in sequences of events to detect hidden patterns that indicate
  possible levels of vulnerability when performing information search tasks in the Internet space.
  A mathematical model of user behavior in a search session based on the theory of sequential patterns
  is described. To improve the computational efficiency of the method, a modified algorithm
  for generating sequential patterns has been developed, at the first stage of which AprioriAll is
  performed, which forms frequent candidate sequences of all possible lengths, and at the second
  stage, a genetic algorithm for optimizing the input parameters of the feature space of the generated
  set to search for maximum patterns. A series of computational experiments were carried out on
  test data from the MSNBC corpus, the SPMF open source data mining library. The comparative
  analysis was carried out with the VMSP and GSP algorithms. The research results confirmed the
  efficiency of the search for maximum sequential patterns by the proposed algorithm in terms of the
  execution time and the number of extracted patterns. The results of the experimental studies of the
  method showed that to increase the stability and accuracy of the work, the sample size obtained as
  a result of the GA operation will reduce the required number of scans of the pattern database,
  providing acceptable computational costs comparable to the VMSP algorithm and the GSP algorithm
  that exceeds the search time for sequential patterns. an average of more than 150 %.

• BASIC APPROACHES TO EXTRACTING TEXTUAL INFORMATION (OVERVIEW)

  V.V. Kureichik, P.S. Gerasimenko

  2024-10-08

  Abstract ▼

  This article is devoted to the review of known and modern approaches, methods and algorithms of
  full-text search. A brief history of the solution of the problem of search in unstructured text data, its development
  and relevance are described. The main task of search in text data is formulated. The definition of
  the database index is given. The target function of the search information system is defined in general
  terms and possible compromise variations of its parameters when solving various applied problems are
  described. A generalized architecture of a modern search information system is given with the division of
  the search problem into two phases: the primary extraction of relevant records and their subsequent ranking
  to form the final search results. The article provides basic descriptions of the main algorithms and
  methods of full-text search, such as: search by terms (logical search), search using trees and their varieties
  (B-trees, UB-trees, tries), search based on n-grams (including search based on frequency representation),
  use of the vector space model (VSM), search based on an inverted (reverse) index, search using the apparatus of fuzzy logic and bioinspired methods. The main advantages and disadvantages of these methods
  are given, their applicability in various conditions is described, and possible methods for optimizing
  the search for text data to improve the accuracy, speed of search and efficiency of resource use are considered.
  Possible promising directions in the field of solving the problem of primary information extraction
  are presented. Some methods for determining the similarity of text records for solving the ranking
  problem based on the apparatus of fuzzy logic are given. The article touches upon the issues of increasing
  the relevance of primary extraction using artificial intelligence methods, neural networks, fuzzy logic and
  bioinspired methods, in particular methods for expanding the search query and/or expanding the processed
  text records. The influence of the boundary conditions of the search system construction on increasing
  its efficiency is described. In conclusion, the article summarizes the review and discusses the prospects
  for further development of various full-text search methods.

• IMPLICIT THREATS IDENTIFICATION BASED ON ANALYSIS OF USER ACTIVITY ON THE INTERNET SPACE

  V. V. Bova , D. Y. Zaporozhets, Y.A. Kravchenko , E. V. Kuliev , V. V. Kureichik , N. A. Lyz

  2020-10-11

  Abstract ▼

  The article is devoted to the problem of identifying implicit information threats of a user's
  search activity in the internet space based on an analysis of his activity in the course of this interaction.
  The use of knowledge stored in the Internet space for the implementation of criminal intentions
  poses a threat to the whole society. Identifying malicious intent in the users’ actions of the
  global information network is not always a trivial task. The proven technologies for analyzing the
  context of user interests fail in the case of cautious and competent actions of attackers who do not
  explicitly demonstrate the goal they are pursuing. The paper analyzes the threats associated with
  certain scenarios for the implementation of search procedures that manifest themselves in search
  activities. Criteria of inefficient and effective search scenarios estimation are described. Among
  the signs indicating the possibility of a threat, the following main ones are highlighted: avoiding
  solving the problem in aimless navigation or attractive resources, superficial search, lack of
  meaningful immersion in solving the search problem, and chaotic actions during the search.
  To determine the presence of adverse signs, a system of indicators is built. The features of an effective
  scenario for organizing a search in the Internet space are formulated, options for the presence
  of implicit threats for a similar situation are described.An approach for identification the
  described threats is presented taking into account the specified criteria for evaluating various
  scenarios of user behavior in the global information space. A machine learning algorithm has
  been developed to identify problem scenarios by comparing with key behavioral patterns. The
  software implementation of the subsystem for identifying information threats has been created,
  experimental studies have been conducted to confirm the effectiveness of the subsystem. Experimental
  studies were carried out on the basis of processing open data from social networks, as well
  as using analysis of user search activity in the university corporate information environment.