A Proactive Trust Evaluation System for Secure Data Collection Based on Sequence Extraction

As a collaborative and open network, billions of devices can be free to join the IoT-based data collection network for data perception and transmission. Along with this trend, more and more malicious attackers enter the network, they steal or tamper with data, and hinder data exchange and communication. To address these issues, we propose a Proactive Trust Evaluation System (PTES) for secure data collection by evaluating the trust of mobile data collectors. Specifically, PTES guarantees evaluation accuracy from trust evidence acquisition, trust evidence storage, and trust value calculation. First, PTES obtains trust evidence based on active detection of drones, feedbacks from interacted objects, and recommendations from trusted third parties. Then, these trust evidences are stored according to interaction time by adopting a sliding window mechanism. After that, credible, untrustworthy, and uncertain evidence sequences are extracted from the storage space, and assigned with positive, negative, and tendentious trust values, respectively. Consequently, the final normalized trust is obtained by combining the three trust values. Finally, extensive experiments conducted on a real-world dataset demonstrate PTES is superior to benchmark methods in terms of detection accuracy and profit.