Dendritic Cell Algorithm with Optimised Parameters using Genetic Algorithm

Noe Nnko, Longzhi Yang, Nitin Naik

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Citations (Scopus)
24 Downloads (Pure)

Abstract

Intrusion detection systems are developed with the abilities to discriminate between normal and anomalous traffic behaviours. The core challenge in implementing an intrusion detection systems is to determine and stop anomalous traffic behavior precisely before it causes any adverse effects to the
network, information systems, or any other hardware and digital assets which forming or in the cyberspace. Inspired by the biological immune system, Dendritic Cell Algorithm (DCA) is a classification algorithm developed for the purpose of anomaly detection based on the danger theory and the functioning of
human immune dendritic cells. In its core operation, DCA uses a weighted sum function to derive the output cumulative values from the input signals. The weights used in this function are either derived empirically from the data or defined by users. Due to this, the algorithm opens the doors for users to specify the weights that may not produce optimal result (often accuracy). This paper proposes a weight optimisation approach implemented using the popular stochastic search tool, genetic algorithm. The approach is validated and evaluated using the KDD99 dataset with promising results generated.
Original languageEnglish
Title of host publication2018 World Congress on Computational Intelligence
PublisherIEEE
ISBN (Electronic)978-1-5090-6017-7
ISBN (Print)978-1-5090-6018-4
DOIs
Publication statusPublished - 8 Jul 2018
EventIEEE World Congress on Computational Intelligence 2018 - Windsor Barra Convention Centre, Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Conference

ConferenceIEEE World Congress on Computational Intelligence 2018
Abbreviated titleWCCI 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

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