As 5-th Generation (5G) mobile communication and edge computing technologies mature, Intelligent Transportation System (ITS) are gradually becoming a reality. In the 5G heterogeneous network, resources such as computing, storage, and communication are allocated to each Road Side Unit (RSU) to provide intelligent services for vehicles. However, the existing average allocation method based on historical experience can easily lead to over-concentration or insufficient resources, which causes waste and reduces the Quality of Service (QoS). To solve this problem, this paper proposes a Multi-Objective Neural Time-series Prediction (M-ONTP) scheme for resource pre-allocation scenario in ITS. The scheme takes into account the complexity and diversity of service resource, innovatively treats the number of vehicles and communication power as joint optimization metrics, and proposes a multi-objective learning model. Benefiting from the vehicle data collected by RSUs in real time, we utilize historical traffic information to predict future road load and rely on Software Defined Network (SDN) to design a flexible resource pre-allocated architecture for ITS. To enhance the effectiveness of feature capture, M-ONTP also organically integrates various neural networks, which can appropriately handle large-scale time-series traffic flow. And we choose two layers of road data for fitting, which ensures that the model has a wide horizon to receive sufficient information. SUMO-based simulation experiments show that our scheme accurately realizes the prediction of joint objective and has significant performance advantage over other models. Meanwhile, our pre-allocation strategy reduces the total resource consumption by about 7%, increases the sufficiency rate by about 7%, and decreases the redundancy by about 12% while ensuring enough service resource to maintain normal QoS, which validates the effectiveness of M-ONTP.

The combination of mobile communication and the Internet of Things (IoT) has made physical devices more intelligent, bringing great convenience to our lives. However, the deep integration of personal information and the Internet increases the risk of data leakage and is easily exploited maliciously. In addition, due to limited system resources, smart devices with lightweight design are required. Therefore, it is necessary to realize low-energy and effective abnormal behavior detection of IoT devices, but the existing detection methods have disadvantages such as leakage of user privacy, low accuracy, and difficulty in dynamically improving the effect. To address these issues, this paper proposes a dynamic interactive minor anomaly detection scheme called ADONIS based on Swarm Learning (SL). The scheme combines the concept of swarm defense and utilizes SL to achieve local data fusion, which improves the detection effect and protects user privacy. Moreover, the decentralized structure of SL can cope with the impact of single node damage to enhance the robustness of IoT services. Furthermore, we propose training and detection decoupling framework to achieve high accuracy, low energy consumption, and low latency. It improves the performance by fitting the training model with full data, and simplifies the complexity of the detection model using knowledge distillation. We also design a self-enhancing dynamic strategy based on the decoupling framework to maintain powerful detection capability through human-computer interaction (HCI) and continuous learning. The framework relies on traffic data to keep the model sensitive to new behavior through iterative training without disturbing the user. Finally, simulation experiments show that our proposed scheme can achieve 82.2% accuracy, reduce the average detection time to 8.22$ ms$, and simplify the model complexity by 15.9%. Compared with existing methods, ADONIS can provide lighter, safer and more accurate anomaly detection.

The Internet of Vehicles (IoV) based on the 6-th Generation (6G) communication brings convenience, but also raises anxiety about information security. Researchers have developed static security schemes based on the blockchain, but it results in excessive resource occupation. And it is difficult to resist some attacks such as desynchronization, Denial of Service (DoS), and covert intrusion. In response to the above problems, this paper proposes the Floating Blockchain Consensus Security (FBCS) scheme. It models the attack risk of Road Side Unit (RSU) through the traffic flow prediction to present the credibility evaluation and constructs the floating blockchain based on the results. And the security capability is adjusted through the dynamic joining and exit mode of trusted nodes and untrusted nodes. FBCS also establishes a relationship between blockchain size and energy consumption. Once the system resources are found to be insufficient, it applies the cloud center-based supplementary certification mechanism to provide authentication to untrusted nodes to enhance the stability of the IoV.Theoretical analysis and simulation experiments prove that the FBCS can afford data privacy, maintain moderate security capabilities, and adapt defense capabilities according to the attack environment, reduce resource occupation to save cost.

The emergence of new communication applications adds high heterogeneity to 5G-networks. With the increase of heterogeneity, handover of user equipment between different service HetNets is frequent. It must smoothly realize user-free switching to provide services continuously. Although the 3 rd Generation Partnership Project (3GPP) has proposed a standard protocol for this scenario, it is found that these protocols cannot satisfy key forward/backward secrecy, lacks mutual authentication, etc. Further, it can be subjected to replay, DoS and other attacks. To alleviate these problems, we propose a mask random array protocol, MRSA. For efficient, secure handover authentication in 5G HetNets, we first design a verification mechanism called mask array, which depends on a random number self-circulating encryption structure. The mechanism can not only check the identity of the communication entity but also evaluate the freshness of the message. Second, we devise the mask array-based key derivation method to ensure the whole mechanism's key security. Third, formal proof and automated analysis are established to verify the efficiency and safety of the proposed MRSA protocol. Finally, function and robustness analysis illustrate the ability to resist attacks, while the simulation base station communication analysis shows the efficiency of the protocol from three aspects of data, time and energy. MRSA has significant performance advantages compared to existing schemes in 5G HetNets.

As city boundaries expand and the vehicles continues to proliferate, the transportation system is increasingly overloaded, greatly increasing people’s commuting burden and extending the resulting negative effects to all areas of work and life. It is a big issue that needs to be solved urgently. However, due to the development of infrastructure and technologies in 6G-driven Intelligent Transportation Systems (ITS), it becomes possible to alleviate urban congestion. Existing solutions either optimize the path planning of each vehicle, or only focus on solving the problem of resource allocation of a single road, neither can take advantage of self-organizing networks and easily fall into local optimum. Combining the above reasons, we propose the Direction Decide as a Service (DDaaS) scheme. First, it contains a novel three-layer service architecture based on Swarm Learning (SL), which enables orderly transmission of traffic data and control instructions and protects user privacy. Second, an improved local model and aggregation method is incorporated into DDaaS, which enables to make accurate predictions when the road resources at a single intersection are insufficient. Third, we propose a dynamic traffic control algorithm to provide signal light switching decisions for rapidly changing ITS. Finally, constructing an urban road simulation experiment combined with SUMO, we prove that DDaaS can reduce traffic congestion effectively and has significant advantages compared to other schemes.

Abstract With the development of mobile communications, the authors step into the 5G era. 5G‐AKA is intended to serve as a certification standard for users to safely and stably enjoy 5G mobile services. However, recent research proves that some attacks may happened based on information deciphering and message replay. In addition, the authors found that some variants of 5G‐AKA are also at risk of linkability attacks. Therefore, in order to solve the above‐mentioned risks, the authors propose a two‐phased, registered protocol TR‐AKA. This protocol finishes two‐way authentication between the user and the home network through only two message‐sending rounds, and a temporary identity variable group is applied to replace the real information for transmission. A newly designed variable is also used to strictly control the increase of the sequence number to promote the sensitivity of freshness. Finally, the authors choose the Tamarin tool to prove that TR‐AKA has achieved authentication and privacy‐protection objectives, and further discussions have also verified the rationality of the protocol design.