Fast Binary Network Intrusion Detection based on Matched Filter Optimization

Hajar Saif Alsaadi, Rachid Hedjam, Abderezak Touzene, Abdelhamid Abdessalem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Securing networks has become very critical task because of the continued appearance of attacks and the growing number of Internet users. The detection, classification and prevention of attacks are provided by the so-called Intrusion Detection System (IDS). In this article, we have proposed and evaluated a new model of network intrusion detection based on matched filter optimization called NIDeMFO for Network Intrusion Detection based on Matched Filter Optimization. Similar to Linear Discriminant Analysis (LDA), the goal is to design a linear filter that projects data into a space where both classes, normal and attack, are well separated. The difference with LDA is that the margin between the averages of the two classes in the projected space is controlled by a parameter. The proposed detection model is evaluated on the NSL-KDD benchmark. The results show its competitiveness and effectiveness compared to many existing detection models.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages195-199
Number of pages5
ISBN (Electronic)9781728148212
DOIs
Publication statusPublished - Feb 2020
Event2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020 - Doha, Qatar
Duration: Feb 2 2020Feb 5 2020

Publication series

Name2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020

Conference

Conference2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
Country/TerritoryQatar
CityDoha
Period2/2/202/5/20

Keywords

  • Anomaly Detection
  • Machine learning
  • Matched Filter
  • Network Intrusion Detection Systems
  • Network security

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management

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