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.
|Title of host publication||2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings|
|Publication status||Published - 4 Oct 2018|
|Event||2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil|
Duration: 8 Jul 2018 → 13 Jul 2018
|Name||2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings|
|Conference||2018 IEEE Congress on Evolutionary Computation, CEC 2018|
|City||Rio de Janeiro|
|Period||8/07/18 → 13/07/18|
Bibliographical noteFunding Information:
This work has been supported by the Commonwealth Scholarship Commission in the United Kingdom (CSC).
- danger theory
- Dendritic cell algorithm
- genetic algorithm
- KDD99 dataset
- network intrusion detection