In the emerging Industrial Internet of Things(IIoT),authentication problems have become an urgent issue for massive resource-constrained devices because traditional costly security mechanisms are not suitable for them...In the emerging Industrial Internet of Things(IIoT),authentication problems have become an urgent issue for massive resource-constrained devices because traditional costly security mechanisms are not suitable for them.The security protocol designed for resource-constrained systems should not only be secure but also efficient in terms of usage of energy,storage,and processing.Although recently many lightweight schemes have been proposed,to the best of our knowledge,they are unable to address the problem of privacy preservation with the resistance of Denial of Service(DoS)attacks in a practical way.In this paper,we propose a lightweight authentication protocol based on the Physically Unclonable Function(PUF)to overcome the limitations of existing schemes.The protocol provides an ingenious authentication and synchronization mechanism to solve the contradictions amount forward secrecy,DoS attacks,and resource-constrained.The performance analysis and comparison show that the proposed scheme can better improve the authentication security and efficiency for resource-constrained systems in IIoT.展开更多
IoT devices rely on authentication mechanisms to render secure message exchange.During data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted...IoT devices rely on authentication mechanisms to render secure message exchange.During data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT devices.The application of physical unclonable functions(PUFs)ensures secure data transmission among the internet of things(IoT)devices in a simplified network with an efficient time-stamped agreement.This paper proposes a secure,lightweight,cost-efficient reinforcement machine learning framework(SLCR-MLF)to achieve decentralization and security,thus enabling scalability,data integrity,and optimized processing time in IoT devices.PUF has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device communication.An IoT network gathers information of interest from multiple cluster members selected by the proposed framework.In addition,the software-defined secured(SDS)technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT platform.Simulation analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime,energy,secured data retrieval rate,and performance ratio.By enabling the proposed framework,number of residual nodes is reduced to 16%,energy consumption is reduced by up to 50%,almost 30%improvement in data retrieval rate,and network lifetime is improved by up to 1000 msec.展开更多
基金This work was supported by China Postdoctoral Science Foundation under Grant Nos.2020M681959 and 2020TQ0291in part by the national key R&D project under Grant No.2018YFB2100401in part by the National Key Research and Development Project No.2018YFB2100400.
文摘In the emerging Industrial Internet of Things(IIoT),authentication problems have become an urgent issue for massive resource-constrained devices because traditional costly security mechanisms are not suitable for them.The security protocol designed for resource-constrained systems should not only be secure but also efficient in terms of usage of energy,storage,and processing.Although recently many lightweight schemes have been proposed,to the best of our knowledge,they are unable to address the problem of privacy preservation with the resistance of Denial of Service(DoS)attacks in a practical way.In this paper,we propose a lightweight authentication protocol based on the Physically Unclonable Function(PUF)to overcome the limitations of existing schemes.The protocol provides an ingenious authentication and synchronization mechanism to solve the contradictions amount forward secrecy,DoS attacks,and resource-constrained.The performance analysis and comparison show that the proposed scheme can better improve the authentication security and efficiency for resource-constrained systems in IIoT.
文摘IoT devices rely on authentication mechanisms to render secure message exchange.During data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT devices.The application of physical unclonable functions(PUFs)ensures secure data transmission among the internet of things(IoT)devices in a simplified network with an efficient time-stamped agreement.This paper proposes a secure,lightweight,cost-efficient reinforcement machine learning framework(SLCR-MLF)to achieve decentralization and security,thus enabling scalability,data integrity,and optimized processing time in IoT devices.PUF has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device communication.An IoT network gathers information of interest from multiple cluster members selected by the proposed framework.In addition,the software-defined secured(SDS)technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT platform.Simulation analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime,energy,secured data retrieval rate,and performance ratio.By enabling the proposed framework,number of residual nodes is reduced to 16%,energy consumption is reduced by up to 50%,almost 30%improvement in data retrieval rate,and network lifetime is improved by up to 1000 msec.
