Hybrid Machine Learning Based Trust Management Approach to Secure the Mobile Crowdsourcing

The Internet of Things (IoT) has become a cornerstone in modern automation and data exchange, with IoT devices increasingly embedded into daily life. This development coincides with a rapid growth in mobile device usage and the proliferation of interactive mobile applications, catalyzing the evolution of mobile crowdsourcing. Such applications offer a range of functionalities, from automated data collection through sensor driven and location aware services to manual input via user surveys and feedback. For mobile crowdsourcing, particularly in anonymous and ever changing environments, modern trust management systems rarely deal with the problem of participants credibility and reliability. This paper presents a hybrid approach that integrates sophisticated trust management techniques with Support Vector Machine (SVM) to improve the security of mobile crowdsourcing platforms. The system considerably enhances the reliability of trust evaluations, successfully protecting against malicious actors. It provides a thorough trustworthiness score to contributors by utilizing a variety of variables, including social networking site data, reputation measures, and user behavior patterns. The high efficacy of our model is demonstrated by an exploratory evaluation of a real mobile crowdsourcing platform, which achieved an accuracy rate of approximately 99.85%.