Dendritic Cell Algorithm Enhancement Using Fuzzy Inference System for Network Intrusion Detection

Noe Elisa, Longzhi Yang, Xin Fu, Nitin Naik

Research output: Chapter in Book/Published conference outputConference publication

Abstract

Dendritic cell algorithm (DCA) is an immune-inspired classification algorithm which is developed for the purpose of anomaly detection in computer networks. The DCA uses a weighted function in its context detection phase to process three categories of input signals including safe, danger and pathogenic associated molecular pattern to three output context values termed as co-stimulatory, mature and semi-mature, which are then used to perform classification. The weighted function used by the DCA requires either manually pre-defined weights usually provided by the immunologists, or empirically derived weights from the training dataset. Neither of these is sufficiently flexible to work with different datasets to produce optimum classification result. To address such limitation, this work proposes an approach for computing the three output context values of the DCA by employing the recently proposed TSK+ fuzzy inference system, such that the weights are always optimal for the provided data set regarding a specific application. The proposed approach was validated and evaluated by applying it to the two popular datasets KDD99 and UNSW NB15. The results from the experiments demonstrate that, the proposed approach outperforms the conventional DCA in terms of classification accuracy.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019
PublisherIEEE
ISBN (Electronic)9781538617281
DOIs
Publication statusPublished - 10 Oct 2019
Event2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019 - New Orleans, United States
Duration: 23 Jun 201926 Jun 2019

Publication series

NameIEEE International Conference on Fuzzy Systems
Volume2019-June
ISSN (Print)1098-7584

Conference

Conference2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019
CountryUnited States
CityNew Orleans
Period23/06/1926/06/19

Bibliographical note

Funding Information:
This work has been supported by the Commonwealth Scholarship Commission (CSC-TZCS-2017-717), and the Industry Academia Partnership Programme by Royal Academy of Engineering (IAPP1\100077).

Keywords

  • Danger theory
  • Dendritic cell algorithm
  • Fuzzy inference systems
  • Network intrusion detection
  • TSK+

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