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Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm.

Zakariyya, Idris; Al-Kadri, M. Omar; Kalutarage, Harsha; Petrovski, Andrei

Authors

Idris Zakariyya

M. Omar Al-Kadri

Harsha Kalutarage



Contributors

Mohammad Obaidat
Editor

Pierangela Samarati
Editor

Abstract

Using Machine Learning (ML) for Internet of Things (IoT) security monitoring is a challenge. This is due to their resource constraint nature that limits the deployment of resource-hungry monitoring algorithms. Therefore, the aim of this paper is to investigate resource consumption reduction of ML algorithms in IoT security monitoring. This paper starts with an empirical analysis of resource consumption of Artificial Immune System (AIS) algorithm, and then employs carefully selected feature reduction techniques to reduce the computational cost of running the algorithm. The proposed approach significantly reduces computational cost as illustrated in the paper. We validate our results using two benchmarks and one purposefully simulated data set.

Citation

ZAKARIYYA, I., AL-KADRI, M.O., KALUTARGE, H. and PETROVSKI, A. 2019. Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm. In Obaidat, M. and Samarati, P. (eds.) Proceedings of the 16th International security and cryptography conference (SECRYPT 2019), co-located with the 16th International joint conference on e-business and telecommunications (ICETE 2019), 26-28 July 2019, Prague, Czech Republic. Setúbal, Portugal: SciTePress [online], 2, pages 523-528. Available from: https://doi.org/10.5220/0008119205230528.

Conference Name 16th International security and cryptography conference (SECRYPT 2019)
Conference Location Prague, Czech Republic
Start Date Jul 26, 2019
End Date Jul 28, 2019
Acceptance Date May 2, 2019
Online Publication Date Oct 24, 2019
Publication Date Oct 31, 2019
Deposit Date Aug 29, 2019
Publicly Available Date Aug 29, 2019
Publisher SciTePress
Volume 2
Pages 523-528
ISBN 9789897583780
DOI https://doi.org/10.5220/0008119205230528
Keywords Computational cost; IoT security; Feature reduction; Resource consumption; Machine learning
Public URL https://rgu-repository.worktribe.com/output/401052

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