Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
PLoS One. 2022 Jul 28;17(7):e0265658. doi: 10.1371/journal.pone.0265658. eCollection 2022.
Every year, millions of new devices are added to the Internet of things, which has both great benefits and serious security risks for user data privacy. It is the device owners' responsibility to ensure that the ownership settings of Internet of things devices are maintained, allowing them to communicate with other user devices autonomously. The ultimate goal of the future Internet of Things is for it to be able to make decisions on its own, without the need for human intervention. Therefore, trust computing and prediction have become more vital in the processing and handling of data as well as in the delivery of services. In this paper, we compute trust in social IoT scenarios using a hybrid approach that combines a distributed computation technique and a global machine learning approach. The approach considers social similarity while assessing other users' ratings and utilize a cloud-based architecture. Further, we propose a dynamic way to aggregate the different computed trust values. According to the results of the experimental work, it is shown that the proposed approaches outperform related work. Besides, it is shown that the use of machine learning provides slightly better performance than the computing model. Both proposed approaches were found successful in degrading malicious ratings without the need for more complex algorithms.
每年，数以百万计的新设备被添加到物联网中，这既为用户数据隐私带来了巨大的好处，也带来了严重的安全风险。确保物联网设备的所有权设置得到维护，使它们能够自主与其他用户设备进行通信，是设备所有者的责任。未来物联网的最终目标是能够自行做出决策，而无需人类干预。因此，信任计算和预测在数据处理和服务交付方面变得更加重要。在本文中，我们使用一种混合方法在社交物联网场景中计算信任，该方法结合了分布式计算技术和全局机器学习方法。该方法在评估其他用户的评分时考虑了社会相似性，并利用了基于云的架构。此外，我们提出了一种动态的方法来聚合不同的计算信任值。根据实验工作的结果，表明所提出的方法优于相关工作。此外，还表明机器学习的使用比计算模型提供了略好的性能。这两种方法都成功地在不使用更复杂算法的情况下降低了恶意评分。
