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Detecting Traffic Anomalies through Aggregate Analysis of Packet Header Data

  • Seong Soo Kim
  • A. L. Narasimha Reddy
  • Marina Vannucci
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3042)

Abstract

If efficient network analysis tools were available, it could become possible to detect the attacks, anomalies and to appropriately take action to contain the attacks. In this paper, we suggest a technique for traffic anomaly detection based on analyzing correlation of destination IP addresses in outgoing traffic at an egress router. This address correlation data are transformed through discrete wavelet transform for effective detection of anomalies through statistical analysis. Our techniques can be employed for postmortem and real-time analysis of outgoing network traffic at a campus edge. Results from trace-driven evaluation suggest that proposed approach could provide an effective means of detecting anomalies close to the network. We also present data analyzing the correlation of port numbers as a means of detecting anomalies.

Keywords

Discrete Wavelet Transform Network Traffic Port Number Aggregate Analysis Simulated Attack 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Seong Soo Kim
    • 1
  • A. L. Narasimha Reddy
    • 1
  • Marina Vannucci
    • 2
  1. 1.Department of Electrical EngineeringUSA
  2. 2.StatisticsTexas A&M UniversityCollege StationUSA

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