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Security Analysis Based on Controlling Dependences of Network Traffic Parameters by Wavelet Transformation

Security Analysis Based on Controlling Dependences of Network Traffic Parameters by Wavelet... ISSN 0146-4116, Automatic Control and Computer Sciences, 2018, Vol. 52, No. 8, pp. 931–935. © Allerton Press, Inc., 2018. Original Russian Text © D.S. Lavrova, I.V. Alekseev, A.A. Shtyrkina, 2018, published in Problemy Informatsionnoi Bezopasnosti, Komp’yuternye Sistemy. Security Analysis Based on Controlling Dependences of Network Traff ic Parameters by Wavelet Transformation a, a, a D. S. Lavrova *, I. V. Alekseev **, and A. A. Shtyrkina Peter the Great S.t Petersburg Polytechnic University, St. Petersburg, 195251 Russia *e-mail: lavrova@ibks.spbstu.ru **e-mail: ialexeev@ibks.spbstu.ru Received March 12, 2018 Abstract—This article presents mathematical tools of wavelet transformations for use in detecting net- work traffic attacks. The technique consists in discrete wavelet transformation of parameters of net- work packets extracted from traffic and tracking the degree of dependence of various network traffic parameters using the multiple correlation coefficient. The efficiency of the proposed technique is shown in the results of experimental detections of SYN f lood DoS attacks. Keywords: backbone network, wavelet analysis, attack detection, multiple correlation, DoS attack DOI: 10.3103/S0146411618080187 INTRODUCTION At present, network attacks are the most popular way of attacking end devices, whether PCs or IoT controllers and sensors. Network attacks allow using vulnerabilities in software installed on devices, exer- cising http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Control and Computer Sciences Springer Journals

Security Analysis Based on Controlling Dependences of Network Traffic Parameters by Wavelet Transformation

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Allerton Press, Inc.
Subject
Computer Science; Control Structures and Microprogramming
ISSN
0146-4116
eISSN
1558-108X
DOI
10.3103/S0146411618080187
Publisher site
See Article on Publisher Site

Abstract

ISSN 0146-4116, Automatic Control and Computer Sciences, 2018, Vol. 52, No. 8, pp. 931–935. © Allerton Press, Inc., 2018. Original Russian Text © D.S. Lavrova, I.V. Alekseev, A.A. Shtyrkina, 2018, published in Problemy Informatsionnoi Bezopasnosti, Komp’yuternye Sistemy. Security Analysis Based on Controlling Dependences of Network Traff ic Parameters by Wavelet Transformation a, a, a D. S. Lavrova *, I. V. Alekseev **, and A. A. Shtyrkina Peter the Great S.t Petersburg Polytechnic University, St. Petersburg, 195251 Russia *e-mail: lavrova@ibks.spbstu.ru **e-mail: ialexeev@ibks.spbstu.ru Received March 12, 2018 Abstract—This article presents mathematical tools of wavelet transformations for use in detecting net- work traffic attacks. The technique consists in discrete wavelet transformation of parameters of net- work packets extracted from traffic and tracking the degree of dependence of various network traffic parameters using the multiple correlation coefficient. The efficiency of the proposed technique is shown in the results of experimental detections of SYN f lood DoS attacks. Keywords: backbone network, wavelet analysis, attack detection, multiple correlation, DoS attack DOI: 10.3103/S0146411618080187 INTRODUCTION At present, network attacks are the most popular way of attacking end devices, whether PCs or IoT controllers and sensors. Network attacks allow using vulnerabilities in software installed on devices, exer- cising

Journal

Automatic Control and Computer Sciences – Springer Journals

Published: Mar 7, 2019

References

  • Detection of DoS and DDoS attacks in information communication networks with discrete wavelet analysis
    Sheluhin, O.I.; Atayero, A.A.
  • Intrusion detection based on wavelet analysis of network traffic
    Tishina, N.A.; Dvorovoi, I.G.; Solov’ev, N.A.
  • Wavelet methods for the detection of anomalies and their application to network traffic analysis
    Kwon, D.W.
  • Detection of anomalous emissions of telecommunication traffic using discrete wavelet analysis methods
    Shelukhin, O.I.; Garmashev, A.V.