A Revised Dendritic Cell Algorithm Using K-Means Clustering

Noe Nnko, Longzhi Yang, Yanpeng Qu, Fei Chao

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

4 Citations (Scopus)
35 Downloads (Pure)

Abstract

The most daunting and challenging task in intrusion detection is to distinguishing between normal and malicious traffics effectively. In order to complete such a task, the biological danger theory has appeared to be one of the most appealing immunological models which has been converted to a computer science algorithm, named as Dendritic Cell Algorithm (DCA). To perform a binary classification, the DCA goes through four phases, preprocessing, detection, context assessment and classification. In particular, the context assessment phase is performed by comparing the signal concentration values between mature (i.e., abnormality) and semi-mature (i.e., normality) contexts. The conventional DCA requires a crisp separation between semi-mature and mature cumulative context values. This can be hard if the difference between the two contexts is marginal, which negatively affects the classification accuracy. In addition, it is technically difficult to quantify the actual meaning of semi-mature and mature in the DCA. This paper proposes an approach that integrates the K-Means clustering algorithm to the DCA to map the DCA cumulative semi-mature and mature context values into semi-mature (normal) and mature (anomaly) clusters in order to improve the classification accuracy. The KDD99 data set was utilized in this work for system validation and evaluation, and the experimental results revealed an improvement in the classification accuracy by the proposed approach.

Original languageEnglish
Title of host publication 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
PublisherIEEE
Pages1547-1554
Number of pages8
ISBN (Electronic)9781538666142
DOIs
Publication statusPublished - 24 Jan 2019
Event20th International Conference on High Performance Computing and Communications, 16th IEEE International Conference on Smart City and 4th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018 - Exeter, United Kingdom
Duration: 28 Jun 201830 Jun 2018

Conference

Conference20th International Conference on High Performance Computing and Communications, 16th IEEE International Conference on Smart City and 4th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018
CountryUnited Kingdom
CityExeter
Period28/06/1830/06/18

Fingerprint

Dive into the research topics of 'A Revised Dendritic Cell Algorithm Using K-Means Clustering'. Together they form a unique fingerprint.

Cite this