As the internet of things(IoT)continues to expand rapidly,the significance of its security concerns has grown in recent years.To address these concerns,physical unclonable functions(PUFs)have emerged as valuable tools...As the internet of things(IoT)continues to expand rapidly,the significance of its security concerns has grown in recent years.To address these concerns,physical unclonable functions(PUFs)have emerged as valuable tools for enhancing IoT security.PUFs leverage the inherent randomness found in the embedded hardware of IoT devices.However,it has been shown that some PUFs can be modeled by attackers using machine-learning-based approaches.In this paper,a new deep learning(DL)-based modeling attack is introduced to break the resistance of complex XAPUFs.Because training DL models is a problem that falls under the category of NP-hard problems,there has been a significant increase in the use of meta-heuristics(MH)to optimize DL parameters.Nevertheless,it is widely recognized that finding the right balance between exploration and exploitation when dealing with complex problems can pose a significant challenge.To address these chal-lenges,a novel migration-based multi-parent genetic algorithm(MBMPGA)is developed to train the deep convolutional neural network(DCNN)in order to achieve a higher rate of accuracy and convergence speed while decreas-ing the run-time of the attack.In the proposed MBMPGA,a non-linear migration model of the biogeography-based optimization(BBO)is utilized to enhance the exploitation ability of GA.A new multi-parent crossover is then introduced to enhance the exploration ability of GA.The behavior of the proposed MBMPGA is examined on two real-world optimization problems.In benchmark problems,MBMPGA outperforms other MH algorithms in convergence rate.The proposed model are also compared with previous attacking models on several simulated challenge-response pairs(CRPs).The simulation results on the XAPUF datasets show that the introduced attack in this paper obtains more than 99%modeling accuracy even on 8-XAPUF.In addition,the proposed MBMPGA-DCNN outperforms the state-of-the-art modeling attacks in a reduced timeframe and with a smaller number of required sets of CRPs.The area under the curve(AUC)of MBMPGA-DCNN outperforms other architectures.MBMPGA-DCNN achieved sensitivities,specificities,and accuracies of 99.12%,95.14%,and 98.21%,respectively,in the test datasets,establishing it as the most successful method.展开更多
How to ensure the security of device access is a common concern in the Internet of Things(IoT)scenario with extremely high device connection density.To achieve efficient and secure network access for IoT devices with ...How to ensure the security of device access is a common concern in the Internet of Things(IoT)scenario with extremely high device connection density.To achieve efficient and secure network access for IoT devices with constrained resources,this paper proposes a lightweight physical-layer authentication protocol based on Physical Unclonable Function(PUF)and channel pre-equalization.PUF is employed as a secret carrier to provide authentication credentials for devices due to its hardware-based uniqueness and unclonable property.Meanwhile,the short-term reciprocity and spatio-temporal uniqueness of wireless channels are utilized to attach an authentication factor related to the spatio-temporal position of devices and to secure the transmission of authentication messages.The proposed protocol is analyzed formally and informally to prove its correctness and security against typical attacks.Simulation results show its robustness in various radio environments.Moreover,we illustrate the advantages of our protocol in terms of security features and complexity through performance comparison with existing authentication schemes.展开更多
As the adoption of Vehicular Ad-hoc Networks(VANETs)grows,ensuring secure communication between smart vehicles and remote application servers(APPs)has become a critical challenge.While existing solutions focus on vari...As the adoption of Vehicular Ad-hoc Networks(VANETs)grows,ensuring secure communication between smart vehicles and remote application servers(APPs)has become a critical challenge.While existing solutions focus on various aspects of security,gaps remain in addressing both high security requirements and the resource-constrained nature of VANET environments.This paper proposes an extended-Kerberos protocol that integrates Physical Unclonable Function(PUF)for authentication and key agreement,offering a comprehensive solution to the security challenges in VANETs.The protocol facilitates mutual authentication and secure key agreement between vehicles and APPs,ensuring the confidentiality and integrity of vehicle-to-network(V2N)communications and preventing malicious data injection.Notably,by replacing traditional Kerberos password authentication with Challenge-Response Pairs(CRPs)generated by PUF,the protocol significantly reduces the risk of key leakage.The inherent properties of PUF—such as unclonability and unpredictability—make it an ideal defense against physical attacks,including intrusion,semi-intrusion,and side-channel attacks.The results of this study demonstrate that this approach not only enhances security but also optimizes communication efficiency,reduces latency,and improves overall user experience.The analysis proves that our protocol achieves at least 86%improvement in computational efficiency compared to some existed protocols.This is particularly crucial in resource-constrained VANET environments,where it enables efficient data transmission between vehicles and applications,reduces latency,and enhances the overall user experience.展开更多
In the context of the diversity of smart terminals,the unity of the root of trust becomes complicated,which not only affects the efficiency of trust propagation,but also poses a challenge to the security of the whole ...In the context of the diversity of smart terminals,the unity of the root of trust becomes complicated,which not only affects the efficiency of trust propagation,but also poses a challenge to the security of the whole system.In particular,the solidification of the root of trust in non-volatile memory(NVM)restricts the system’s dynamic updating capability,which is an obvious disadvantage in a rapidly changing security environment.To address this issue,this study proposes a novel approach to generate root security parameters using static random access memory(SRAM)physical unclonable functions(PUFs).SRAM PUFs,as a security primitive,show great potential in lightweight security solutions due to their inherent physical properties,low cost and scalability.However,the stability of SRAM PUFs in harsh environments is a key issue.These environmental conditions include extreme temperatures,high humidity,and strong electromagnetic radiation,all of which can affect the performance of SRAM PUFs.In order to ensure the stability of root safety parameters under these conditions,this study proposes an integrated approach that covers not only the acquisition of entropy sources,but also the implementation of algorithms and configuration management.In addition,this study develops a series of reliability-enhancing algorithms,including adaptive parameter selection,data preprocessing,auxiliary data generation,and error correction,which are essential for improving the performance of SRAM PUFs in harsh environments.Based on these techniques,this study establishes six types of secure parameter generation mechanisms,which not only improve the security of the system,but also enhance its adaptability in variable environments.Through a series of experiments,we verify the effectiveness of the proposed method.Under 10 different environmental conditions,our method is able to achieve full recovery of security data with an error rate of less than 25%,which proves the robustness and reliability of our method.These results not only provide strong evidence for the stability of SRAM PUFs in practical applications,but also provide a new direction for future research in the field of smart terminal security.展开更多
文摘不经意传输(OT,oblivious transfer)协议是密码学中的一个基本协议。基于物理不可克隆函数(PUF,physical unclonable function)给出物理不可克隆函数系统(PUFS,physical unclonable function system)的概念,并在此基础上提出一个新的不经意传输协议(POT,PUFS based OT),最后在通用可组合(UC,universal composition)框架内给出POT协议抵抗静态敌手的安全性证明。相比于传统基于公钥加密的OT方案,POT协议不使用任何可计算的假设,而是基于PUFS的安全属性实现,因此在很大程度上减小了计算和通信开销。
文摘As the internet of things(IoT)continues to expand rapidly,the significance of its security concerns has grown in recent years.To address these concerns,physical unclonable functions(PUFs)have emerged as valuable tools for enhancing IoT security.PUFs leverage the inherent randomness found in the embedded hardware of IoT devices.However,it has been shown that some PUFs can be modeled by attackers using machine-learning-based approaches.In this paper,a new deep learning(DL)-based modeling attack is introduced to break the resistance of complex XAPUFs.Because training DL models is a problem that falls under the category of NP-hard problems,there has been a significant increase in the use of meta-heuristics(MH)to optimize DL parameters.Nevertheless,it is widely recognized that finding the right balance between exploration and exploitation when dealing with complex problems can pose a significant challenge.To address these chal-lenges,a novel migration-based multi-parent genetic algorithm(MBMPGA)is developed to train the deep convolutional neural network(DCNN)in order to achieve a higher rate of accuracy and convergence speed while decreas-ing the run-time of the attack.In the proposed MBMPGA,a non-linear migration model of the biogeography-based optimization(BBO)is utilized to enhance the exploitation ability of GA.A new multi-parent crossover is then introduced to enhance the exploration ability of GA.The behavior of the proposed MBMPGA is examined on two real-world optimization problems.In benchmark problems,MBMPGA outperforms other MH algorithms in convergence rate.The proposed model are also compared with previous attacking models on several simulated challenge-response pairs(CRPs).The simulation results on the XAPUF datasets show that the introduced attack in this paper obtains more than 99%modeling accuracy even on 8-XAPUF.In addition,the proposed MBMPGA-DCNN outperforms the state-of-the-art modeling attacks in a reduced timeframe and with a smaller number of required sets of CRPs.The area under the curve(AUC)of MBMPGA-DCNN outperforms other architectures.MBMPGA-DCNN achieved sensitivities,specificities,and accuracies of 99.12%,95.14%,and 98.21%,respectively,in the test datasets,establishing it as the most successful method.
基金supported by National Natural Science Foundation of China(No.61931020,No.U19B2024 and No.62371462).
文摘How to ensure the security of device access is a common concern in the Internet of Things(IoT)scenario with extremely high device connection density.To achieve efficient and secure network access for IoT devices with constrained resources,this paper proposes a lightweight physical-layer authentication protocol based on Physical Unclonable Function(PUF)and channel pre-equalization.PUF is employed as a secret carrier to provide authentication credentials for devices due to its hardware-based uniqueness and unclonable property.Meanwhile,the short-term reciprocity and spatio-temporal uniqueness of wireless channels are utilized to attach an authentication factor related to the spatio-temporal position of devices and to secure the transmission of authentication messages.The proposed protocol is analyzed formally and informally to prove its correctness and security against typical attacks.Simulation results show its robustness in various radio environments.Moreover,we illustrate the advantages of our protocol in terms of security features and complexity through performance comparison with existing authentication schemes.
基金supported in part by the Jiangsu“Qing Lan Project”,Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Major Research Project:23KJA520007)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCX25_1303).
文摘As the adoption of Vehicular Ad-hoc Networks(VANETs)grows,ensuring secure communication between smart vehicles and remote application servers(APPs)has become a critical challenge.While existing solutions focus on various aspects of security,gaps remain in addressing both high security requirements and the resource-constrained nature of VANET environments.This paper proposes an extended-Kerberos protocol that integrates Physical Unclonable Function(PUF)for authentication and key agreement,offering a comprehensive solution to the security challenges in VANETs.The protocol facilitates mutual authentication and secure key agreement between vehicles and APPs,ensuring the confidentiality and integrity of vehicle-to-network(V2N)communications and preventing malicious data injection.Notably,by replacing traditional Kerberos password authentication with Challenge-Response Pairs(CRPs)generated by PUF,the protocol significantly reduces the risk of key leakage.The inherent properties of PUF—such as unclonability and unpredictability—make it an ideal defense against physical attacks,including intrusion,semi-intrusion,and side-channel attacks.The results of this study demonstrate that this approach not only enhances security but also optimizes communication efficiency,reduces latency,and improves overall user experience.The analysis proves that our protocol achieves at least 86%improvement in computational efficiency compared to some existed protocols.This is particularly crucial in resource-constrained VANET environments,where it enables efficient data transmission between vehicles and applications,reduces latency,and enhances the overall user experience.
基金supported by National key Research and Development Program“Security Protection Technology for Critical Information Infrastructure of Distribution Network”(2022YFB3105100).
文摘In the context of the diversity of smart terminals,the unity of the root of trust becomes complicated,which not only affects the efficiency of trust propagation,but also poses a challenge to the security of the whole system.In particular,the solidification of the root of trust in non-volatile memory(NVM)restricts the system’s dynamic updating capability,which is an obvious disadvantage in a rapidly changing security environment.To address this issue,this study proposes a novel approach to generate root security parameters using static random access memory(SRAM)physical unclonable functions(PUFs).SRAM PUFs,as a security primitive,show great potential in lightweight security solutions due to their inherent physical properties,low cost and scalability.However,the stability of SRAM PUFs in harsh environments is a key issue.These environmental conditions include extreme temperatures,high humidity,and strong electromagnetic radiation,all of which can affect the performance of SRAM PUFs.In order to ensure the stability of root safety parameters under these conditions,this study proposes an integrated approach that covers not only the acquisition of entropy sources,but also the implementation of algorithms and configuration management.In addition,this study develops a series of reliability-enhancing algorithms,including adaptive parameter selection,data preprocessing,auxiliary data generation,and error correction,which are essential for improving the performance of SRAM PUFs in harsh environments.Based on these techniques,this study establishes six types of secure parameter generation mechanisms,which not only improve the security of the system,but also enhance its adaptability in variable environments.Through a series of experiments,we verify the effectiveness of the proposed method.Under 10 different environmental conditions,our method is able to achieve full recovery of security data with an error rate of less than 25%,which proves the robustness and reliability of our method.These results not only provide strong evidence for the stability of SRAM PUFs in practical applications,but also provide a new direction for future research in the field of smart terminal security.
