Anomaly Detection Framework Based on Matching Pursuit for Network Security Enhancement

Abstract

In this paper, a framework for recognizing network traffic in order to detect anomalies is proposed. We propose to combine and correlate parameters from different layers in order to detect 0-day attacks and reduce False Positives. Moreover, we propose to combine statistical and signal-based features. The major contribution of this paper are: novel framework for network security based on the correlation approach as well as new signal based algorithm for intrusion detection using Matching Pursuit.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2010
Accession Number
ADA584056

Entities

People

  • Rafal Renk
  • Witold Holubowicz

Organizations

  • Adam Mickiewicz University in Poznań

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Anomaly Detection
  • Change Detection
  • Computer Network Security
  • Computer Networks
  • Cyber Defense Techniques
  • Cybersecurity
  • Detection
  • Detectors
  • Intrusion
  • Intrusion Detection
  • Intrusion Detection Systems
  • Intrusion Detectors
  • Networks
  • Security
  • Signal Processing
  • Transport Protocols

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.

Technology Areas

  • Cyber