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

Nnko, Noe, Yang, Longzhi, Fu, Xin and Naik, Nitin (2019) Dendritic Cell Algorithm Enhancement Using Fuzzy Inference System for Network Intrusion Detection. In: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, Piscataway, NJ, pp. 1-6. ISBN 9781538617281

PID5871841.pdf - Accepted Version

Download (584kB) | Preview


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.

Item Type: Book Section
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Noe Nnko
Date Deposited: 08 Apr 2019 12:33
Last Modified: 31 Jul 2021 17:46

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics