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Intrusion Detection in NEAR System by Anti-denoising Traffic Data Series using Discrete Wavelet TransformVANCEA, F.
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discrete wavelet transform, intrusion detection, self-similarity, signal denoising, time-frequency analysis
traffic(9), wavelet(8), processing(7), network(7), detection(7), signal(6), analysis(4)
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About this article
Date of Publication: 2014-11-30
Volume 14, Issue 4, Year 2014, On page(s): 43 - 48
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.04007
Web of Science Accession Number: 000348772500007
SCOPUS ID: 84921631227
The paper presents two methods for detecting anomalies in data series derived from network traffic. Intrusion detection systems based on network traffic analysis are able to respond to incidents never seen before by detecting anomalies in data series extracted from the traffic. Some anomalies manifest themselves as pulses of various sizes and shapes, superimposed on series corresponding to normal traffic. In order to detect those impulses we propose two methods based on discrete wavelet transformation. Their effectiveness expressed in relative thresholds on pulse amplitude for no false negatives and no false positives is then evaluated against pulse duration and Hurst characteristic of original series. Different base functions are also evaluated for efficiency in the context of the proposed methods.
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 A new approach for internet traffic classification: GA-WK-ELM, Ertam, Fatih, Avcı, Engin, Measurement, ISSN 0263-2241, Issue , 2017.
Digital Object Identifier: 10.1016/j.measurement.2016.10.001 [CrossRef]
 Some results on intrusion and anomaly detection using signal processing and NEAR system, Vancea, Florin, Vancea, Codruta, 2015 38th International Conference on Telecommunications and Signal Processing (TSP), ISBN 978-1-4799-8498-5, 2015.
Digital Object Identifier: 10.1109/TSP.2015.7296234 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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