Karelova O. L. (Doctor of Physical and Mathematical Sciences, Associate Professor, Professor at the Department of International IT Security Moscow State Linguistic University; Professor at the Department of Applied Information Technologies, The Russian Presidential Academy of National Economy and Public Administration (Moscow))
Kostrova O. E. (Institute of Information Sciences at Moscow State Linguistic University (Moscow))
Kurbanova K. M. (Institute of Information Sciences at Moscow State Linguistic University (Moscow))
| |
This article examines the classification of intrusion detection systems (IDS) and the effectiveness of applying various machine learning and deep learning algorithms in these systems. The purpose of intrusion detection systems, their main functions, methods for detecting intrusions, types, and operating principles are also described. Statistics on the performance of traditional intrusion detection systems based on open-source solutions are provided, as well as an examination of the effectiveness of applying different machine learning and deep learning algorithms in detecting various types of attacks on network infrastructures.
Keywords:intrusion detection system, attack, machine learning, traffic analysis, Suricata, Snort
|
|
| |
|
Read the full article …
|
Citation link:
Karelova O. L., Kostrova O. E., Kurbanova K. M. ANALYSIS OF THE EFFECTIVENESS OF MACHINE LEARNING MODELS IN INTRUSION DETECTION SYSTEMS // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2025. -№02/2. -С. 103-107 DOI 10.37882/2223-2966.2025.02-2.19 |
|
|
