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Network Anomaly Detection Based on Wavelet Analysis

Abstract

Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

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Correspondence to Wei Lu.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Lu, W., Ghorbani, A.A. Network Anomaly Detection Based on Wavelet Analysis. EURASIP J. Adv. Signal Process. 2009, 837601 (2008). https://doi.org/10.1155/2009/837601

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Keywords

  • Intrusion Detection
  • Anomaly Detection
  • Signal Processing Technique
  • Attack Type
  • Wavelet Approximation
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