In this paper, we present a comprehensive approach that leverages the capabilities of blockchain technology to protect private data in 6G UAV networks. We propose a dual-layer structure UAV swarm model based on blockchain, aiming to enhance the security during collaborative mission execution and data sharing between UAVs, and ensure data integrity through the decentralized ledger of blockchain security and access control. In addition, we study a dynamic trust assessment method that can evaluate the trustworthiness of UAV nodes, demonstrate adaptability to network fluctuations and potential attacks by evaluating various trust parameters and dynamically adjusting trust propagation and update strategies, while combining district Blockchain technology to identify and mitigate malicious attacks. We also investigate a tailored lightweight blockchain solution designed for UAV networks with constrained resources, aiming to boost efficiency via streamlined consensus protocols, intelligent allocation of computing resources, and decentralized storage strategies. The result is a safe and efficient data sharing platform customized to meet the unique needs of UAVs. Together, these strategies provide a powerful framework to fully protect private data in 6G UAV networks, leveraging the security and decentralization provided by blockchain technology.

A mobile edge computing (MEC)-enabled blockchain system is proposed in this study for secure data storage and sharing in internet of things (IoT) networks, with the MEC acting as an overlay system to provide dynamic computation offloading services. Considering latency-critical, resource-limited, and dynamic IoT scenarios, an adaptive system resource allocation and computation offloading scheme is designed to optimize the scalability performance for MEC-enabled blockchain systems, wherein the scalability is quantified as MEC computational efficiency and blockchain system throughput. Specifically, we jointly optimize the computation offloading policy and block generation strategy to maximize the scalability of MEC-enabled blockchain systems and meanwhile guarantee data security and system efficiency. In contrast to existing works that ignore frequent user movement and dynamic task requirements in IoT networks, the joint performance optimization scheme is formulated as a Markov decision process (MDP). Furthermore, we design a deep deterministic policy gradient (DDPG)-based algorithm to solve the MDP problem and define the multiple and variable number of consecutive time slots as a decision epoch to conduct model training. Specifically, DDPG can solve an MDP problem with a continuous action space and it only requires a straightforward actor–critic architecture, making it suitable for tackling the dynamics and complexity of the MEC-enabled blockchain system. As demonstrated by simulations, the proposed scheme can achieve performance improvements over the deep Q network (DQN)-based scheme and some other greedy schemes in terms of long-term transactional throughput.

The development of the Internet of Vehicles (IoV) has significantly enhanced connectivity and cooperation among road entities, leading to a more efficient, economical, and safer intelligent transportation system (ITS). However, this increased connectivity also exposes vehicles to a growing risk of cybersecurity threats through intra-vehicle and inter-vehicle networks. To secure IoV networks, many studies have focused on using intrusion detection systems (IDSs) based on deep learning methods to effectively detect cyber-attacks due to their ability to learn from large-scale data. Nonetheless, most existing IDSs rely on expert knowledge to manually design features, resulting in difficulties adapting to evolving attacks and information loss. To mitigate these limitations, this paper presents a tokenization representation and attention mechanism-based Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) intrusion detection method. For feature extraction, we tokenize original traffic using natural language processing technique to represent discrete hexadecimal bytes as words, thus eliminating the need for manual feature design and allowing for the direct extraction of sequence patterns. For classification, we incorporate an attention mechanism into the CNN and BiLSTM architectures to enhance the accuracy of intrusion detection by focusing on critical information and capturing sequential patterns. The effectiveness of the proposed IDS is evaluated in both intra-vehicle and inter-vehicle network scenarios. Experimental results show that our method can detect various types of attacks with 100% accuracy on the Car-Hacking dataset for the intra-vehicle network scenario. In the inter-vehicle network scenario utilizing the CICIoT2023 dataset, our approach also achieves a high accuracy of 98%, outperforming existing methods.

Resource constraints have prevented comprehensive cryptography and multifactor authentication in numerous Internet of Things (IoT) connectivity scenarios. Existing IoT systems generally adopt lightweight security protocols that lead to compromise and privacy leakage. Edge computing enables better access control and privacy protection, furthermore, blockchain architecture has achieved a trusted store of value by open-source and distributed consensus mechanisms. To embrace these new paradigms, we propose a scheme that employs one-time association multitasking proofs for peer to local authentication (OTMP-P2L). The scheme chooses relevant nondeterministic polynomial (NP) problem tasks, and manages localized trust and anonymity by using smart devices such as phones and pads, thereby enabling IoT devices to autonomously perform consensus validation with an enhanced message authentication code. This nested code is a one-time zero-knowledge proof that comprises multiple logic verification arguments. To increase diversity and reduce the workload of each one, these arguments are chained by a method that establishes some of the inputs of the following task from the output of previous tasks. We implemented a smart lock system and confirmed that the scheme outperforms IoT authentication methods. The result demonstrates superior flexibility through dynamic difficulty strategies and succinct non-interactive peer-to-peer (P2P) verification.