PyWSNSim: A Python-based component-oriented simulation framework for sinkhole attack analysis in large-scale WSNs

Large-scale wireless sensor networks (WSNs), consisting of hundreds to thousands of sensor nodes, are vulnerable to malicious threats such as sinkhole attacks. While several frameworks for simulating WSNs have been proposed to defend against malicious threats, existing simulators have limitations when it comes to configuring large-scale environments, modeling sinkhole attacks, and building standardized attack datasets. These difficulties hinder the development of effective attack detection and defense mechanisms. To overcome these limitations, this paper proposes PyWSNSim, a component-centric Python-based simulation framework to simulate and analyze sinkhole attacks in large-scale WSNs. PyWSNSim provides scalability for environment parameters, routing protocols, and attack models in a sensor network. The proposed framework transforms the behavior of the network in various sinkhole attack scenarios into a dataset. This dataset not only guarantees the reproducibility of attacks but is also utilized as a training dataset for machine learning-based network threat detection models.