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Packet Header Anomaly Detection Using Statistical Analysis

  • Warusia Yassin
  • Nur Izura Udzir
  • Azizol Abdullah
  • Mohd Taufik Abdullah
  • Zaiton Muda
  • Hazura Zulzalil
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 299)

Abstract

The disclosure of network packets to recurrent cyber intrusion has upraised the essential for modelling various statistical-based anomaly detection methods lately. Theoretically, the statistical-based anomaly detection method fascinates researcher’s attentiveness, but technologically, the fewer intrusion detection rates persist as vulnerable disputes. Thus, a Host-based Packet Header Anomaly Detection (HbPHAD) model that is proficient in pinpoint suspicious packet header behaviour based on statistical analysis is proposed in this paper. We perform scoring mechanism using Relative Percentage Ratio (RPR) in scheming normal scores, desegregate Linear Regression Analysis (LRA) to distinguish the degree of packets behaviour (i.e. fit to be suspicious or not suspicious) and Cohen’s-d (effect size) dimension to pre-define the finest threshold. HbPHAD is an effectual resolution for statistical-based anomaly detection method in pinpoint suspicious behaviour precisely. The experiment validate that HbPHAD is effectively in correctly detecting suspicious packet at above 90% as an intrusion detection rate for both ISCX 2012 and is capable to detect 40 attack types from DARPA 1999 benchmark dataset.

Keywords

Packet Header Anomaly Detection Statistical Analysis Linear Regression Analysis Cohen’s-d 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Warusia Yassin
    • 1
    • 2
  • Nur Izura Udzir
    • 1
  • Azizol Abdullah
    • 1
  • Mohd Taufik Abdullah
    • 1
  • Zaiton Muda
    • 1
  • Hazura Zulzalil
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSelangorMalaysia
  2. 2.Faculty of Information and Communication TechnologyUniversiti Teknikal MalaysiaMelakaMalaysia

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