Vol.38, No.2, 2021, pp.183-195, doi:10.32604/csse.2021.015275
A Cuckoo Search Detector Generation-based Negative Selection Algorithm
  • Ayodele Lasisi1,*, Ali M. Aseere2
1 Department of Mathematical Sciences, Faculty of Science, Augustine University, Ilara-Epe, Lagos, Nigeria
2 Department and College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia
* Corresponding Author: Ayodele Lasisi. Email:
Received 14 November 2020; Accepted 02 February 2021; Issue published 23 April 2021
The negative selection algorithm (NSA) is an adaptive technique inspired by how the biological immune system discriminates the self from non-self. It asserts itself as one of the most important algorithms of the artificial immune system. A key element of the NSA is its great dependency on the random detectors in monitoring for any abnormalities. However, these detectors have limited performance. Redundant detectors are generated, leading to difficulties for detectors to effectively occupy the non-self space. To alleviate this problem, we propose the nature-inspired metaheuristic cuckoo search (CS), a stochastic global search algorithm, which improves the random generation of detectors in the NSA. Inbuilt characteristics such as mutation, crossover, and selection operators make the CS attain global convergence. With the use of Lévy flight and a distance measure, efficient detectors are produced. Experimental results show that integrating CS into the negative selection algorithm elevated the detection performance of the NSA, with an average increase of 3.52% detection rate on the tested datasets. The proposed method shows superiority over other models, and detection rates of 98% and 99.29% on Fisher’s IRIS and Breast Cancer datasets, respectively. Thus, the generation of highest detection rates and lowest false alarm rates can be achieved.
Negative selection algorithm; detector generation; cuckoo search; optimization
Cite This Article
A. Lasisi and A. M. Aseere, "A cuckoo search detector generation-based negative selection algorithm," Computer Systems Science and Engineering, vol. 38, no.2, pp. 183–195, 2021.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.