In recent years, with the development of intelligent vehicles and wireless sensor network technology, the research on road safety has attracted much attention in vehicular ad-hoc networks. By sensing events on the road, vehicles can broadcast information to inform others of traffic jams or accidents. However, the mobile vehicle network has a large transmission delay, which makes real-time content transmission impossible. In this paper, a new certificateless aggregate signcryption scheme is proposed by using a fog computing framework that supports mobility, low latency and location awareness. It is combined with online/offline encryption technology, which reduces many time-consuming operations and improves the security of vehicle users and the efficiency of message authentication. In addition, the scheme has the characteristics of mutual authentication, anonymity, untraceability, and non-deniability. Based on the difficulty of the discrete logarithm problem (DLP) and the computational Diffie-Hellman (CDH) problem, the scheme is further proved to be unforgeability and confidentiality under the random oracle model. Simulation results show that compared with the existing schemes, this scheme can not only ensure the security requirements of the system, but also achieve higher efficiency in computing and communication.

With the technology of data storage and shared services in the cloud, users can easily share data or revoke from their groups. To ensure the accuracy of the shared data, the key is used for group users to calculate the authenticators of the shared data and audit the data's integrity remotely. However, high-speed mobile terminals have posed challenges to key management in Vehicle Ad-hoc networks (VANETs). Besides, to maintain the integrity of the shared data, the computation and communication overheads must be increased in the revocation process when a vehicle user is removed from a group. For such, a new shared data auditing scheme is proposed in this paper, which supports user revocation by combining the existing certificateless signcryption method with a fog architecture. This scheme improves the efficiency of auditing and user revocation since it reduces many time-consuming operations, as well as features such as public auditing, mutual authentication, efficient and secure revocation are presented. Security analysis and experimental results demonstrate that the proposed scheme is both correct and safe, has more comprehensive functions and better efficiency than other existing ones.

Data sharing in Vehicular Social Networks (VSNs) is an essential road service that assists vehicle driving and promotes intelligent transportation applications. In VSNs, vehicles regularly collect and upload valuable data to share with other vehicles. Data encryption can be employed during data uploading and sharing to prevent malicious tampering and privacy disclosure. However, existing data-sharing schemes lack security, have high overhead in obtaining decrypted data, and show low trust in the central authority controlling the entire network. To facilitate data sharing in VSNs, this paper proposes a new scheme using consortium blockchain to realize secure data sharing. Nodes in the blockchain invoke smart contracts and implement the location-based Speculative Byzantine Fault Tolerance (LSBFT) to accomplish data-sharing transactions among vehicles. The scheme not only ensures the security of vehicle information but also protects the privacy of the shared data. Security analysis demonstrates that the proposed scheme can resist attacks and has shown transaction fairness, data confidentiality, non-repudiation, and traceability. Simulation results show that the scheme has higher sharing efficiency and less time to reach a consensus in the data storage process.

Intrusion detection is an important means to protect system security by detecting intrusions or intrusion attempts on the system through operational behaviors, security logs, and data audit. However, existing intrusion detection systems suffer from incomplete data feature extraction and low classification accuracy, which affects the intrusion detection effect. To this end, this paper proposes an intrusion detection model that fuses residual network (RESNET) and parallel crossconvolutional neural network, called RESNETCCN. RESNETCNN can efficiently learn various data stream features through the fusion of deep learning and convolutional neural network (CNN), which improves the detection accuracy of abnormal data streams in unbalanced data streams, moreover, the oversampling method into the data preprocessing, to extract multiple types of unbalanced data stream features at the same time, effectively solving the problems of incomplete data feature extraction and low classification accuracy of unbalanced data streams. Finally, three improved versions of RESNETCNN networks are designed to meet the requirements of different traffic data processing, and the highest detection accuracy reaches 99.98% on the CICIDS 2017 dataset and 99.90% on the ISCXIDS 2012 dataset.

In this paper, a cutting-edge video target tracking system is proposed, combining feature location and blockchain technology. The location method makes full use of feature registration and received trajectory correction signals to achieve high accuracy in tracking targets. The system leverages the power of blockchain technology to address the challenge of insufficient accuracy in tracking occluded targets, by organizing the video target tracking tasks in a secure and decentralized manner. To further enhance the accuracy of small target tracking, the system uses adaptive clustering to guide the target location process across different nodes. In addition, the paper also presents an unmentioned trajectory optimization post-processing approach, which is based on result stabilization, effectively reducing inter-frame jitter. This post-processing step plays a crucial role in maintaining a smooth and stable track of the target, even in challenging scenarios such as fast movements or significant occlusions. Experimental results on CarChase2 (TLP) and basketball stand advertisements (BSA) datasets show that the proposed feature location method is better than the existing methods, achieving a recall of 51% (27.96+) and a precision of 66.5% (40.04+) in the CarChase2 dataset and recall of 85.52 (11.75+)% and precision of 47.48 (39.2+)% in the BSA dataset. Moreover, the proposed video target tracking and correction model performs better than the existing tracking model, showing a recall of 97.1% and a precision of 92.6% in the CarChase2 dataset and an average recall of 75.9% and mAP of 82.87% in the BSA dataset, respectively. The proposed system presents a comprehensive solution for video target tracking, offering high accuracy, robustness, and stability. The combination of robust feature location, blockchain technology, and trajectory optimization post-processing makes it a promising approach for a wide range of video analytics applications, such as surveillance, autonomous driving, and sports analysis.