GALTrust: Generative Adverserial Learning-Based Framework for Trust Management in Spatial Crowdsourcing Drone Services
Junaid Akram, Ali Anaissi, Rajkumar Singh Rathore, Rutvij H. Jhaveri, Awais Akram
Abstract
In the evolving landscape of consumer electronics, the Generative Adversarial Learning-based Trust Management (GALTrust) framework emerges as a novel solution, uniquely combining Generative Adversarial Networks (GANs) and type-2 fuzzy logic to tackle trust management challenges within the Internet of Drone Things (IoDT). Addressing the pivotal needs of spatial crowdsourcing scenarios like bushfire management, GALTrust significantly overcomes the limitations posed by traditional machine learning methods in detecting emergent types of malicious nodes and navigating the impact of training data size variations. At its core, GALTrust features a GAN-based codec structure, meticulously trained with trust vectors, enabling precise differentiation between malicious and trustworthy nodes. A key innovation of GALTrust is the introduction of a GAN-based trust redemption model, strategically designed to curtail false positives and safeguard against the unwarranted exclusion of benign drones, thus markedly enhancing network resilience. This framework exhibits dynamic adaptability, continually refining its trust model to align with the latest detection insights within the IoDT ecosystem. Through its application in secure clustering for IoDT, GALTrust has proven its efficacy by achieving an exceptional detection rate of up to 94.1% and maintaining a false positive rate below 9.1%, thereby significantly elevating security and operational efficiency in crucial consumer electronics applications.