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.
|Title of host publication||2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019|
|Publication status||Published - 10 Oct 2019|
|Event||2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019 - New Orleans, United States|
Duration: 23 Jun 2019 → 26 Jun 2019
|Name||IEEE International Conference on Fuzzy Systems|
|Conference||2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019|
|Period||23/06/19 → 26/06/19|
Bibliographical noteFunding 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).
- Danger theory
- Dendritic cell algorithm
- Fuzzy inference systems
- Network intrusion detection