Meta-heuristics algorithms (MHA) have been applied with success to many optimization problems in various domains including wireless communication. This paper proposes the application of nature-inspired MHA with a different angle: as an instrument to minimize the impact of perturbations to the network based features, which are used to classify WiFi Network saturation conditions. The context is the presence of an attacker, which aims to alter the values of the network features using a randomization algorithm to negatively impact the identification of the WiFi Network saturation condition. The approach proposed in this paper is to minimize the impact of the attack by using MHAs to compute weights to minimize the perturbations. The MHAs are used in combination with the K-means clustering algorithm to enhance their applicability. The approach is applied to a public WiFi Network saturation data set using the Random Forest classifier. The results show that the proposed approach is able to enhance the classification performance with more than 10% accuracy improvement and that the improvement is more significant when the perturbations are particularly severe.

BALDINI Gianmarco; 

2026-10-01

IEEE

JRC145400

979-8-3195-4344-8 (online),   

2687-9808 (online),   

https://publications.jrc.ec.europa.eu/repository/handle/JRC145400,   

10.1109/BlackSeaCom69760.2026.11598167 (online),