Repository logo
Communities & Collections
All of CRUST
Statistics
English
Yкраїнська
New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Mehelbei, Yehor O."

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Results Per Page
  • Sort Options
  • Loading...
    Thumbnail Image
    Item type:Item,
    Detection of Attacks of the U2R Category by Means of the SOM on Database NSL-KDD
    (Український державний університет науки і технологій, ННІ «Інститут промислових та бізнес технологій», ІВК «Системні технології», Дніпро, 2022) Pakhomova, Victoria M.; Mehelbei, Yehor O.
    ENG: Creating an effective system for detecting network attacks requires the use of qualitatively new approaches to information processing, which should be based on adaptive algorithms capable of self-learning. The mathematical apparatus of the Kohonen self-organizing map (SOM) was used as a research method. Python language with a wide range of modern standard tools was used as a software implementation of the Kohonen SOM addition, this section compiles the Python software model «SOM_U2R» using a Kohonen SOM. Created «SOM_U2R» software model on database NSL-KDD an error research was performed for different number of epochs with different map sizes. On the «SOM_U2R» model the research of parameters of quality of detection of attacks is carried out. It is determined that on the «SOM_U2R» created software model the error of the second kind of detection of network classes of attacks Buffer_overflow and Rootkit is 6 %, and for the class Loadmodule reached 16 %. In addition, a survey of the Fmeasure was conducted for a different number of epochs of learning the Kohonen SOM. It is determined that for all network attack classes (except Buffer_overflow) the F-measure increases, reaching its maximum value at 50 epochs.

DSpace software copyright © 2002-2026 LYRASIS

  • Accessibility settings
  • Privacy policy
  • End User Agreement
  • Send Feedback
Repository logo COAR Notify