Unmanned Aerial Vehicle (UAV) swarms not only expanded the application areas of UAVs but also brought more complex challenges to security. Both the data collected by UAVs and UAVs themselves are easy targets for hackers. Once a hacker has intercepted communication information or hijacked a UAV, it can cause the disclosure of information and even affect the normal flight of the UAV. In terms of authentication, existing research focuses on building peer-to-peer networks through devices that support blockchain deployments at the edge nodes to form blockchain networks. UAVs cannot run blockchains directly, so fog nodes need to be introduced to assist UAVs in achieving authentication. This paper proposes a UAV authentication solution based on the fog node-assisted blockchain. The solution incorporates smart contracts to ensure trust, along with on-chain registration and authentication for UAVs. In the design of this paper, the main use is made of fog nodes at the edge of each block. The fog nodes serve as agents to register and authenticate the UAVs in their area on the blockchain, thus enabling the authentication of UAVs in different fog node areas. In this paper, the analysis of gas consumption yields a total economic benefit cost of about $$\yen $$¥ 12.77, which is negligible for the safety of the UAVs. Meanwhile, the paper compares the computational overheads, and within 10 UAVs, the computational overhead of this scheme is less than 50 ms, which is significantly lower than the other two schemes.

Network attack traffic detection plays a crucial role in protecting network operations and services. To accurately detect malicious traffic on the internet, this paper designs a hybrid algorithm UMAP-RF for both binary and multiclassification network attack detection tasks. First, the network traffic data are dimensioned down with UMAP algorithm. The random forest algorithm is improved based on parameter optimization, and the improved random forest algorithm is used to classify the network traffic data, distinguishing normal data from abnormal data and classifying nine different types of network attacks from the abnormal data. Experimental results on the UNSW-NB15 dataset, which are significant improvements compared to traditional machine-learning methods, show that the UMAP-RF hybrid model can perform network attack traffic detection effectively, with accuracy and recall rates of 92.6% and 91%, respectively.

Many time-sensitive scenarios need to decrypt data at a specified time. The timed-release encryption (TRE) primitive can meet this requirement. However, in the single-time server TRE model, there is a single point of failure problem. Therefore, we propose a tamper-resistant timed secure data transmission protocol based on smart contracts. Firstly, by decomposing the ciphertext into ciphertext fragments, the amount of deposit that a single middleman needs to submit is reduced. Secondly, it provides the system with security redundancy that changes with the decomposition mode. Thirdly, the sender is required to submit the hash value of each ciphertext fragment to the blockchain network at the same time as sending data, so that the receiver can quickly verify the authenticity of the ciphertext to resist substitution attack. Security analysis shows that the proposed protocol model can resist interruption attacks, release-ahead attacks and replacement attacks. Finally, we conduct a monetary cost test on the Ethereum’s Rinkeby test network. The results show that our running cost is almost double compared with the existing similar scheme, but it is still very low and almost negligible compared with the value of the content and the expected profits it brings.