{"title":"Security situation assessment in UAV swarm networks using TransReSE: A Transformer-ResNeXt-SE based approach","authors":"Dongmei Zhao , Pengcheng Shen , Xunzhen Han , Shuiguang Zeng","doi":"10.1016/j.vehcom.2024.100842","DOIUrl":null,"url":null,"abstract":"<div><p>With the rapid development and extensive application of unmanned aerial vehicles (UAVs), the issue of UAV swarm network security has become prominent. To protect the security of UAV swarm networks, effective network security defense measures are crucial. One key aspect is the assessment and monitoring of the network's security situation. However, most existing research focuses on the security of individual UAVs or detecting specific attacks, which fails to provide proactive protection for the network. To address this issue, we propose a UAV swarm network security situation assessment method, which combines the Transformer network with the optimization of the Aggregated Residual Transformations for Deep Neural Networks (ResNeXt) and squeeze-and-excitation (SE) structure (named TransReSE). By using multiple scale-cross convolution kernels, TransReSE can efficiently extract data features and improve situation assessment accuracy through the Transformer network. Experimental results from four public datasets have shown that TransReSE outperforms other schemes in terms of accuracy, recall, and F1. By assessing the value of the swarm network situation and the threat level, we can make faster, more effective decisions and proactively allocate resources to defend against UAV swarm network attacks.</p></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100842"},"PeriodicalIF":5.8000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Vehicular Communications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214209624001177","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
With the rapid development and extensive application of unmanned aerial vehicles (UAVs), the issue of UAV swarm network security has become prominent. To protect the security of UAV swarm networks, effective network security defense measures are crucial. One key aspect is the assessment and monitoring of the network's security situation. However, most existing research focuses on the security of individual UAVs or detecting specific attacks, which fails to provide proactive protection for the network. To address this issue, we propose a UAV swarm network security situation assessment method, which combines the Transformer network with the optimization of the Aggregated Residual Transformations for Deep Neural Networks (ResNeXt) and squeeze-and-excitation (SE) structure (named TransReSE). By using multiple scale-cross convolution kernels, TransReSE can efficiently extract data features and improve situation assessment accuracy through the Transformer network. Experimental results from four public datasets have shown that TransReSE outperforms other schemes in terms of accuracy, recall, and F1. By assessing the value of the swarm network situation and the threat level, we can make faster, more effective decisions and proactively allocate resources to defend against UAV swarm network attacks.
期刊介绍:
Vehicular communications is a growing area of communications between vehicles and including roadside communication infrastructure. Advances in wireless communications are making possible sharing of information through real time communications between vehicles and infrastructure. This has led to applications to increase safety of vehicles and communication between passengers and the Internet. Standardization efforts on vehicular communication are also underway to make vehicular transportation safer, greener and easier.
The aim of the journal is to publish high quality peer–reviewed papers in the area of vehicular communications. The scope encompasses all types of communications involving vehicles, including vehicle–to–vehicle and vehicle–to–infrastructure. The scope includes (but not limited to) the following topics related to vehicular communications:
Vehicle to vehicle and vehicle to infrastructure communications
Channel modelling, modulating and coding
Congestion Control and scalability issues
Protocol design, testing and verification
Routing in vehicular networks
Security issues and countermeasures
Deployment and field testing
Reducing energy consumption and enhancing safety of vehicles
Wireless in–car networks
Data collection and dissemination methods
Mobility and handover issues
Safety and driver assistance applications
UAV
Underwater communications
Autonomous cooperative driving
Social networks
Internet of vehicles
Standardization of protocols.