With the development of Internet of Things (IoT) technology, various types of information, such as social resources and physical resources, are deeply integrated for different comprehensive applications. Social networking, car networking, medical services, video surveillance and other forms of the IoT information service model gradually change people’s daily lives. Facing the vast amounts of IoT information data, IoT search technology is used to quickly find accurate information to meet the real-time search needs of users. However, IoT search requires using a large amount of user private information, such as personal health information, location information and social relations information, to provide personalized services. Employing private information from users will encounter security problems if an effective access control mechanism is missing during the IoT search process. An access control mechanism can effectively monitor the access activities of resources and ensure that authorized users access information resources under legitimate conditions. This survey examines the growing literature on access control for an IoT search. Problems and challenges of access control mechanisms are analyzed to facilitate the adoption of access control solutions in real-life settings. This paper aims to provide theoretical, methodological and technical guidance for IoT search access control mechanisms in large-scale dynamic heterogeneous environments. Based on a literature review, we also analyzed the future development direction of access control in the age of IoT.

Edge computing provides high-class intelligent services and computing capabilities at the edge of the networks. The aim is to ease the backhaul impacts and offer an improved user experience. However, the edge artificial intelligence exacerbates the security of the cloud computing environment due to the dissociation of data, access control, and service stages. In order to prevent users from carrying out lateral movement attacks in an edge-cloud computing environment, in this paper we propose a real-time lateral movement detection method, named CloudSEC, based on an evidence reasoning network for the edge-cloud environment. First, the concept of vulnerability correlation is introduced. Based on the vulnerability knowledge and environmental information of the network system, the evidence reasoning network is constructed, and the lateral movement reasoning ability provided by the evidence reasoning network is then used. The experiment results show that CloudSEC provides a strong guarantee for the rapid and effective evidence investigation, as well as real-time attack detection.

Conducting reputation management is very important for Internet of vehicles. However, most of the existing work evaluate the effectiveness of their schemes with settled attacking behaviors in their simulation, which cannot represent the real scenarios. In this paper, we propose to consider dynamical and diversity attacking strategies in the simulation of reputation management scheme evaluation. To that end, we apply evolutionary game theory to model the evolution process of malicious users’ attacking strategies, and discuss the methodology of the evaluation simulations. We further apply our evaluation method to a reputation management scheme with multiple utility functions, and discuss the evaluation results. The results indicate that our evaluation method is able to depict the evolving process of the dynamic attacking strategies in a vehicular network, and the final state of the simulation could be used to quantify the protection effectiveness of the reputation management scheme.

With the development of the Internet of Things (IoT) and big data technology, the intelligent transportation system is becoming the main development direction of future transportation systems. The time required for a given path in a transportation system can be accurately estimated using the trajectory data of the taxis in a city. This is a very challenging task. Although historical data has been used in existing research, excessive use of trace information in historical data or inaccurate neighbor trajectory information does not allow for better prediction accuracy of the query path. We propose a deep learning method based on neighbors for travel time estimation (TTE), called the Nei-TTE method. We divide the entire path into multiple disjoint segments and use the historical trajectory data approximated at the time level. Our model captures the characteristics of each segment and utilizes the trajectory characteristics of adjacent segments as the road network topology and speed interact. We use velocity features to effectively represent adjacent segment structures. The experiments on the Porto dataset show that the experimental results of our model are significantly better than those of the existing models.

Trust management of Internet of connected vehicles has been a hot topic during the recent years with the rapid development of UGV technologies. However, existing resolutions based on trustworthiness verification among vehicles make the traffic event transmission quite inefficient. In this paper, we assume that the deployed RSUs can provide efficient communication between any pair of RSU and vehicle, and propose Vcash, a reputation framework for identifying denial of traffic service, to resolve the trustworthiness problem in the application level of the Internet of connected vehicles. In our reputation framework, every vehicle communicates with the RSU directly for traffic event verification, and spread verified traffic event notification. We borrow the idea of market trading, and set up trading rules to restrict the malicious vehicle’s spread of false message, and to encourage vehicles to contribute to the traffic event monitoring and verification. To evaluate the effectiveness of our reputation framework, we conduct simulation experiment. Our experiment results indicate that our proposal manages to avoid bogus event spread, and a vehicle in our framework has to contribute to the traffic event detection to normally employ the traffic service.