Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm
This repository contains the code the code employed in the empirical evaluation of feature reduction methods, namely Principal Component Analysis (PCA) and Gini Index (GI), in terms of resource reduction while running computationally expensive Machine Learning (ML) algorithms. The ML technique of choice for this project is the Artificial Immune System, which appears to be resource-intensive in nature.
Citing this work
If you use this repository for academic research, you are highly encouraged to cite the following paper:
@article{zakariyya2019reducing,
title={Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm.},
author={Zakariyya, Idris and Al-Kadri, M Omar and Kalutarage, Harsha and Petrovski, Andrei},
year={2019},
publisher={SciTePress}
}