Keywords
network traffic analysis
sniffing
self-similarity analysis
multifractal analysis
sniffing
self-similarity analysis
multifractal analysis
Abstract
The aim of this work was computer network traffic analysis. Theoretical part de-scribes issues referring to network traffic capture software, time-series classification using Hurst exponent and multifractal spectrum creating methods. In research part was made an analysis of network traffic based on a number of packets and data transfer speed. It was also made a Hurst exponent analysis and a multifractal spectrum analysis for each type of analyzed network traffic. After the research it was possible to draw conclusions about characteristic of analyzed network traffic.
References
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[3] Qian B., Rasheed K.: Hurst exponent and financial market predictability, University of Georgia, 2005.
[4] Dymora P., Mazurek M., Network Anomaly Detection Based on the Statistical Selfsimilarity Factor, Analysis and Simulation of Electrical and Computer Systems Lecture Notes in Electrical Engineering Volume 324, Springer, pp 271-287, 2015.
[5] Mazurek M., Dymora P., Network anomaly detection based on the statistical selfsimilarity factor for HTTP protocol, Przegląd elektrotechniczny, ISSN 0033-2097, R. 90 NR 1/2014, s.127 - 130, 2014.
[6] Brożek B., Dymora P., Mazurek M., Badanie wydajności systemu operacyjnego zainfekowanego złośliwym oprogramowaniem z wykorzystaniem analizy samopodobieństwa, Zeszyty Naukowe Politechniki Rzeszowskiej 294, Elektrotechnika 35 RUTJEE, t. XXIV, z. 35 (2/16), kwiecień-czerwiec 2016, (p-ISSN 0209-2662, eISSN 2300-6358).
[7] http://analytics-magazine.org/the-hurst-exponent-predictability-of-time-series/, [Dostęp 18.04.2017].
[8] Jędruś S.: Modele multifraktalne natężenia ruchu sieciowego z uwzględnieniem samopodobieństwa statystycznego. Telekomunikacja Cyfrowa: technologie i usługi T.4, 2001/2002.