Esraa M. Ghourab , Shimaa Naser , Sami Muhaidat , Lina Bariah , Mahmoud Al-Qutayri , Ernesto Damiani , Paschalis C. Sofotasios
{"title":"用于在车载网络中安全选择中继器的移动目标防御方法","authors":"Esraa M. Ghourab , Shimaa Naser , Sami Muhaidat , Lina Bariah , Mahmoud Al-Qutayri , Ernesto Damiani , Paschalis C. Sofotasios","doi":"10.1016/j.vehcom.2024.100774","DOIUrl":null,"url":null,"abstract":"<div><p>Ensuring the security and reliability of cooperative vehicle-to-vehicle (V2V) communications is an extremely challenging task due to the dynamic nature of vehicular networks as well as the delay-sensitive wireless medium. In this context, the moving target defense (MTD) paradigm has been proposed to overcome the challenges of conventional solutions based on static network services and configurations. Specifically, the MTD approach involves the dynamic altering of network configurations to improve resilience to cyberattacks. Nevertheless, the current MTD solution for cooperative networks poses several limitations, such as that they require high synchronization modules that are resource-intensive and difficult to implement; and they rely heavily on attack-defense models, which may not always be accurate or comprehensive to use. To overcome these challenges, the proposed approach introduces an adaptive defense strategy within the MTD framework. This strategy proposes an intelligent spatiotemporal diversification-based MTD scheme to defend against eavesdropping attacks in cooperative V2V networks. It involves altering the system configuration spatially through relay selection and adjusting the percentage of injected fake data over time. This approach aims to balance reducing intercept probability while ensuring high throughput. Our methodology involves modeling the configuration of vehicular relays and data injection patterns as a Markov decision process, followed by applying deep reinforcement learning to determine the optimal configuration. We then iteratively evaluate the intercept probability and the percentage of transmitted real data for each configuration until convergence is achieved. To optimize the security-real data percentage (S-RDP), we developed a two-agent framework, namely MTD-DQN-RSS & MTD-DQN-RSS-RDP. The first agent, MTD-DQN-RSS, tries to minimize the intercept probability by injecting additional fake data, which in turn reduces the overall RDP, while the second agent, MTD-DQN-RSS-RDP, attempts to inject a sufficient amount of fake data to achieve a target S-RDP. Finally, extensive simulation results are conducted to demonstrate the effectiveness of our proposed solution, which improved system security compared to the conventional relay selection approach.</p></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":null,"pages":null},"PeriodicalIF":5.8000,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Moving target defense approach for secure relay selection in vehicular networks\",\"authors\":\"Esraa M. Ghourab , Shimaa Naser , Sami Muhaidat , Lina Bariah , Mahmoud Al-Qutayri , Ernesto Damiani , Paschalis C. Sofotasios\",\"doi\":\"10.1016/j.vehcom.2024.100774\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Ensuring the security and reliability of cooperative vehicle-to-vehicle (V2V) communications is an extremely challenging task due to the dynamic nature of vehicular networks as well as the delay-sensitive wireless medium. In this context, the moving target defense (MTD) paradigm has been proposed to overcome the challenges of conventional solutions based on static network services and configurations. Specifically, the MTD approach involves the dynamic altering of network configurations to improve resilience to cyberattacks. Nevertheless, the current MTD solution for cooperative networks poses several limitations, such as that they require high synchronization modules that are resource-intensive and difficult to implement; and they rely heavily on attack-defense models, which may not always be accurate or comprehensive to use. To overcome these challenges, the proposed approach introduces an adaptive defense strategy within the MTD framework. This strategy proposes an intelligent spatiotemporal diversification-based MTD scheme to defend against eavesdropping attacks in cooperative V2V networks. It involves altering the system configuration spatially through relay selection and adjusting the percentage of injected fake data over time. This approach aims to balance reducing intercept probability while ensuring high throughput. Our methodology involves modeling the configuration of vehicular relays and data injection patterns as a Markov decision process, followed by applying deep reinforcement learning to determine the optimal configuration. We then iteratively evaluate the intercept probability and the percentage of transmitted real data for each configuration until convergence is achieved. To optimize the security-real data percentage (S-RDP), we developed a two-agent framework, namely MTD-DQN-RSS & MTD-DQN-RSS-RDP. The first agent, MTD-DQN-RSS, tries to minimize the intercept probability by injecting additional fake data, which in turn reduces the overall RDP, while the second agent, MTD-DQN-RSS-RDP, attempts to inject a sufficient amount of fake data to achieve a target S-RDP. Finally, extensive simulation results are conducted to demonstrate the effectiveness of our proposed solution, which improved system security compared to the conventional relay selection approach.</p></div>\",\"PeriodicalId\":54346,\"journal\":{\"name\":\"Vehicular Communications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2024-04-16\",\"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/S2214209624000494\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Vehicular Communications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214209624000494","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}
Moving target defense approach for secure relay selection in vehicular networks
Ensuring the security and reliability of cooperative vehicle-to-vehicle (V2V) communications is an extremely challenging task due to the dynamic nature of vehicular networks as well as the delay-sensitive wireless medium. In this context, the moving target defense (MTD) paradigm has been proposed to overcome the challenges of conventional solutions based on static network services and configurations. Specifically, the MTD approach involves the dynamic altering of network configurations to improve resilience to cyberattacks. Nevertheless, the current MTD solution for cooperative networks poses several limitations, such as that they require high synchronization modules that are resource-intensive and difficult to implement; and they rely heavily on attack-defense models, which may not always be accurate or comprehensive to use. To overcome these challenges, the proposed approach introduces an adaptive defense strategy within the MTD framework. This strategy proposes an intelligent spatiotemporal diversification-based MTD scheme to defend against eavesdropping attacks in cooperative V2V networks. It involves altering the system configuration spatially through relay selection and adjusting the percentage of injected fake data over time. This approach aims to balance reducing intercept probability while ensuring high throughput. Our methodology involves modeling the configuration of vehicular relays and data injection patterns as a Markov decision process, followed by applying deep reinforcement learning to determine the optimal configuration. We then iteratively evaluate the intercept probability and the percentage of transmitted real data for each configuration until convergence is achieved. To optimize the security-real data percentage (S-RDP), we developed a two-agent framework, namely MTD-DQN-RSS & MTD-DQN-RSS-RDP. The first agent, MTD-DQN-RSS, tries to minimize the intercept probability by injecting additional fake data, which in turn reduces the overall RDP, while the second agent, MTD-DQN-RSS-RDP, attempts to inject a sufficient amount of fake data to achieve a target S-RDP. Finally, extensive simulation results are conducted to demonstrate the effectiveness of our proposed solution, which improved system security compared to the conventional relay selection approach.
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.