DOS and Brute Force attacks faults detection using an optimised Fuzzy C-means

Karwan Qader, Mo Adda

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

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Abstract

This paper explains how the commonly occurring DOS and Brute Force attacks on computer networks can be efficiently detected and network performance improved, which reduces costs and time. Therefore, network administrators attempt to instantly diagnose any network issues. The experimental work used the SNMP-MIB parameter datasets, which are collected via a specialised MIB dataset consisting of seven types of attack as noted in section three. To resolves such issues, this researched carried out several important contributions which are related to fault management concerns in computer network systems. A central task in the detection of the attacks relies on MIB feature behaviours using the suggested SFCM method. It was concluded that the DOS and Brute Force fault detection results for three different clustering methods demonstrated that the proposed SFCM detected every data point in the related group. Consequently, the FPC approached 1.0, its highest record, and an improved performance solution better than the EM methods and K-means are based on SNMP-MIB variables. Index Terms—Fault detection, Fuzzy Cluster Means, Network Fault Attacks, Subtractive Clustering, Fault Clustering
Original languageEnglish
Title of host publication2019 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-7281-1862-8, 978-1-7281-1861-1
ISBN (Print)978-1-7281-1863-5
DOIs
Publication statusPublished - 29 Jul 2019
EventIEEE INISTA 2019: International Symposium on Innovations in Intelligent Systems and Applications - Sofia, Bulgaria
Duration: 3 Jul 20195 Jul 2019

Conference

ConferenceIEEE INISTA 2019
Country/TerritoryBulgaria
CitySofia
Period3/07/195/07/19

Keywords

  • fault detection
  • fuzzy cluster means
  • network fault attacks
  • subtractive clustering
  • fault clustering

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