Car manufacturers aim to enhance the use of two-factor authentication (2FA) to protect keyless entry systems in contemporary cars. Despite providing significant ease for users, keyless entry systems have become more s...Car manufacturers aim to enhance the use of two-factor authentication (2FA) to protect keyless entry systems in contemporary cars. Despite providing significant ease for users, keyless entry systems have become more susceptible to appealing attacks like relay attacks and critical fob hacking. These weaknesses present considerable security threats, resulting in unauthorized entry and car theft. The suggested approach combines a conventional keyless entry feature with an extra security measure. Implementing multi-factor authentication significantly improves the security of systems that allow keyless entry by reducing the likelihood of unauthorized access. Research shows that the benefits of using two-factor authentication, such as a substantial increase in security, far outweigh any minor drawbacks.展开更多
To ensure the access security of 6G,physical-layer authentication(PLA)leverages the randomness and space-time-frequency uniqueness of the channel to provide unique identity signatures for transmitters.Furthermore,the ...To ensure the access security of 6G,physical-layer authentication(PLA)leverages the randomness and space-time-frequency uniqueness of the channel to provide unique identity signatures for transmitters.Furthermore,the introduction of artificial intelligence(AI)facilitates the learning of the distribution characteristics of channel fingerprints,effectively addressing the uncertainties and unknown dynamic challenges in wireless link modeling.This paper reviews representative AI-enabled PLA schemes and proposes a graph neural network(GNN)-based PLA approach in response to the challenges existing methods face in identifying mobile users.Simulation results demonstrate that the proposed method outperforms six baseline schemes in terms of authentication accuracy.Furthermore,this paper outlines the future development directions of PLA.展开更多
With the rapid development and widespread adoption of Internet of Things(IoT)technology,the innovative concept of the Internet of Vehicles(IoV)has emerged,ushering in a new era of intelligent transportation.Since vehi...With the rapid development and widespread adoption of Internet of Things(IoT)technology,the innovative concept of the Internet of Vehicles(IoV)has emerged,ushering in a new era of intelligent transportation.Since vehicles are mobile entities,they move across different domains and need to communicate with the Roadside Unit(RSU)in various regions.However,open environments are highly susceptible to becoming targets for attackers,posing significant risks of malicious attacks.Therefore,it is crucial to design a secure authentication protocol to ensure the security of communication between vehicles and RSUs,particularly in scenarios where vehicles cross domains.In this paper,we propose a provably secure cross-domain authentication and key agreement protocol for IoV.Our protocol comprises two authentication phases:intra-domain authentication and cross-domain authentication.To ensure the security of our protocol,we conducted rigorous analyses based on the ROR(Real-or-Random)model and Scyther.Finally,we show in-depth comparisons of our protocol with existing ones from both security and performance perspectives,fully demonstrating its security and efficiency.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
As a model for the next generation of the Internet,the metaverse—a fully immersive,hyper-temporal virtual shared space—is transitioning from imagination to reality.At present,the metaverse has been widely applied in...As a model for the next generation of the Internet,the metaverse—a fully immersive,hyper-temporal virtual shared space—is transitioning from imagination to reality.At present,the metaverse has been widely applied in a variety of fields,including education,social entertainment,Internet of vehicles(IoV),healthcare,and virtual tours.In IoVs,researchers primarily focus on using the metaverse to improve the traffic safety of vehicles,while paying limited attention to passengers’social needs.At the same time,Social Internet ofVehicles(SIoV)introduces the concept of social networks in IoV to provide better resources and services for users.However,the problem of single interaction between SIoVand users has become increasingly prominent.In this paper,we first introduce a SIoVenvironment combined with the metaverse.In this environment,we adopt blockchain as the platform of the metaverse to provide a decentralized environment.Concerning passengers’social data may contain sensitive/private information,we then design an authentication and key agreement protocol calledMSIoV-AKAto protect the communications.Through formal security verifications in the real-or-random(ROR)model and using the AVISPA(Automated Validation of Internet Security Protocols and Applications)tool,we firmly verify the security of the protocol.Finally,detailed comparisons are made between our protocol and robust protocols/schemes in terms of computational cost and communication cost.In addition,we implement the MSIoV-AKA protocol in the Ethereum test network and Hyperledger Sawtooth to show the practicality.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
To date,many previous studies have been proposed for driver authentication;however,these solutions have many shortcomings and are still far from practical for real-world applications.In this paper,we tackle the shortc...To date,many previous studies have been proposed for driver authentication;however,these solutions have many shortcomings and are still far from practical for real-world applications.In this paper,we tackle the shortcomings of the existing solutions and reach toward proposing a lightweight and practical authentication system,dubbed DriveMe,for identifying drivers on cars.Our novelty aspects are 1⃝Lightweight scheme that depends only on a single sensor data(i.e.,pressure readings)attached to the driver’s seat and belt.2⃝Practical evaluation in which one-class authentication models are trained from only the owner users and tested using data collected from both owners and attackers.3⃝Rapid Authentication to quickly identify drivers’identities using a few pressure samples collected within short durations(1,2,3,5,or 10 s).4⃝Realistic experiments where the sensory data is collected from real experiments rather than computer simulation tools.We conducted real experiments and collected about 13,200 samples and 22,800 samples of belt-only and seat-only datasets from all 12 users under different settings.To evaluate system effectiveness,we implemented extensive evaluation scenarios using four one-class detectors One-Class Support Vector Machine(OCSVM),Local Outlier Factor(LOF),Isolation Forest(IF),and Elliptic Envelope(EE),three dataset types(belt-only,seat-only,and fusion),and four different dataset sizes.Our average experimental results show that the system can authenticate the driver with an F1 score of 93.1%for seat-based data using OCSVM classifier,an F1 score of 98.53%for fusion-based data using LOF classifier,an F1 score of 91.65%for fusion-based data using IF classifier,and an F1 score of 95.79%for fusion-based data using EE classifier.展开更多
The Internet of Things(IoT)is extensively applied across various industrial domains,such as smart homes,factories,and intelligent transportation,becoming integral to daily life.Establishing robust policies for managin...The Internet of Things(IoT)is extensively applied across various industrial domains,such as smart homes,factories,and intelligent transportation,becoming integral to daily life.Establishing robust policies for managing and governing IoT devices is imperative.Secure authentication for IoT devices in resource-constrained environments remains challenging due to the limitations of conventional complex protocols.Prior methodologies enhanced mutual authentication through key exchange protocols or complex operations,which are impractical for lightweight devices.To address this,our study introduces the privacy-preserving software-defined range proof(SDRP)model,which achieves secure authentication with low complexity.SDRP minimizes the overhead of confidentiality and authentication processes by utilizing range proof to verify whether the attribute information of a user falls within a specific range.Since authentication is performed using a digital ID sequence generated from indirect personal data,it can avoid the disclosure of actual individual attributes.Experimental results demonstrate that SDRP significantly improves security efficiency,increasing it by an average of 93.02%compared to conventional methods.It mitigates the trade-off between security and efficiency by reducing leakage risk by an average of 98.7%.展开更多
The integration of artificial intelligence(AI)with advanced power technologies is transforming energy system management,particularly through real-time data monitoring and intelligent decision-making driven by Artifici...The integration of artificial intelligence(AI)with advanced power technologies is transforming energy system management,particularly through real-time data monitoring and intelligent decision-making driven by Artificial Intelligence Generated Content(AIGC).However,the openness of power system channels and the resource-constrained nature of power sensors have led to new challenges for the secure transmission of power data and decision instructions.Although traditional public key cryptographic primitives can offer high security,the substantial key management and computational overhead associated with these primitives make them unsuitable for power systems.To ensure the real-time and security of power data and command transmission,we propose a lightweight identity authentication scheme tailored for power AIGC systems.The scheme utilizes lightweight symmetric encryption algorithms,minimizing the resource overhead on power sensors.Additionally,it incorporates a dynamic credential update mechanism,which can realize the rotation and update of temporary credentials to ensure anonymity and security.We rigorously validate the security of the scheme using the Real-or-Random(ROR)model and AVISPA simulation,and the results show that our scheme can resist various active and passive attacks.Finally,performance comparisons and NS3 simulation results demonstrate that our proposed scheme offers enhanced security features with lower overhead,making it more suitable for power AIGC systems compared to existing solutions.展开更多
In the rapidly evolving landscape of intelligent transportation systems,the security and authenticity of vehicular communication have emerged as critical challenges.As vehicles become increasingly interconnected,the n...In the rapidly evolving landscape of intelligent transportation systems,the security and authenticity of vehicular communication have emerged as critical challenges.As vehicles become increasingly interconnected,the need for robust authentication mechanisms to safeguard against cyber threats and ensure trust in an autonomous ecosystem becomes essential.On the other hand,using intelligence in the authentication system is a significant attraction.While existing surveys broadly address vehicular security,a critical gap remains in the systematic exploration of Deep Learning(DL)-based authentication methods tailored to these communication paradigms.This survey fills that gap by offering a comprehensive analysis of DL techniques—including supervised,unsupervised,reinforcement,and hybrid learning—for vehicular authentication.This survey highlights novel contributions,such as a taxonomy of DL-driven authentication protocols,real-world case studies,and a critical evaluation of scalability and privacy-preserving techniques.Additionally,this paper identifies unresolved challenges,such as adversarial resilience and real-time processing constraints,and proposes actionable future directions,including lightweight model optimization and blockchain integration.By grounding the discussion in concrete applications,such as biometric authentication for driver safety and adaptive key management for infrastructure security,this survey bridges theoretical advancements with practical deployment needs,offering a roadmap for next-generation secure intelligent vehicular ecosystems for the modern world.展开更多
The northern structural belt of Kuqa Depression is adjacent to the South Tianshan orogenic belt, which are characterized by complex geological conditions. The reservoir quality of the Jurassic Ahe Formation is control...The northern structural belt of Kuqa Depression is adjacent to the South Tianshan orogenic belt, which are characterized by complex geological conditions. The reservoir quality of the Jurassic Ahe Formation is controlled by sedimentation, diagenesis, and tectonics, and show complex pore structure and strong heterogeneity, thereby hindering effective natural gas exploration and development. Core, thin sections, cathodoluminescence (CL), scanning electron microscopy (SEM), conventional well logs and image logs are used to characterize the petrological characteristics and pore systems. Then a comprehensive analysis integrating sedimentation, diagenesis, and tectonics is performed to unravel the reservoir formation mechanism and distribution of reservoir quality. Results show that reservoir properties are generally environmentally selective. Coarse grained sandbodies (gravelly sandstones) formed in high depositional-energy have the best physical properties, while fine sandstone and mudstone with low depositional energy is easily to be tightly compacted, and have poor reservoir quality. Porosity usually decreases with compaction and cementation, and increases due to dissolution. Clay minerals filling pores result in a deterioration of the pore structure. Microfracture formed by fracturing can connect the matrix pores, effectively improving the reservoirs’ permeability. The differential distribution of fractures and in-situ stress plays an important role in modifying reservoir quality. The in-situ stress has obvious control over the matrix physical properties and fracture effectiveness. The matrix physical properties are negatively correlated with the value of horizontal stress difference (Δσ). As the value of Δσ increases, the pore structure becomes more complex, and the macroscopic reservoir quality becomes poor. The smaller the strike divergence between the natural fracture and SHmax, the lower the value of Δσ in the fracture layers is, and the better the fracture effectiveness is. Under the control of ternary factors on the reservoir, sedimentation-diagenesis jointly affect the matrix reservoir quality, while fractures and in-situ stress caused by tectonism affect the permeability and hydrocarbon productivity of the reservoir. Affected by ternary factors, reservoir quality and hydrocarbon productivity show obvious differences within the various structural location. Reservoir quality in tight sandstones can be predicted by integrating sedimentation, diagenesis, and tectonics (fracture and in-situ stress) in a compressional tectonic setting like Kuqa Depression. The research results will provide insights into the efficient exploration of oil and gas in Kuqa Depression as well as similar compressional tectonic settings elsewhere.展开更多
Physical layer authentication(PLA)in the context of the Internet of Things(IoT)has gained significant attention.Compared with traditional encryption and blockchain technologies,PLA provides a more computationally effi...Physical layer authentication(PLA)in the context of the Internet of Things(IoT)has gained significant attention.Compared with traditional encryption and blockchain technologies,PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself.Some existing PLA solutions rely on static mechanisms,which are insufficient to address the authentication challenges in fifth generation(5G)and beyond wireless networks.Additionally,with the massive increase in mobile device access,the communication security of the IoT is vulnerable to spoofing attacks.To overcome the above challenges,this paper proposes a lightweight deep convolutional neural network(CNN)equipped with squeeze and excitation module(SE module)in dynamic wireless environments,namely SE-ConvNet.To be more specific,a convolution factorization is developed to reduce the complexity of PLA models based on deep learning.Moreover,an SE module is designed in the deep CNN to enhance useful features andmaximize authentication accuracy.Compared with the existing solutions,the proposed SE-ConvNet enabled PLA scheme performs excellently in mobile and time-varying wireless environments while maintaining lower computational complexity.展开更多
文摘Car manufacturers aim to enhance the use of two-factor authentication (2FA) to protect keyless entry systems in contemporary cars. Despite providing significant ease for users, keyless entry systems have become more susceptible to appealing attacks like relay attacks and critical fob hacking. These weaknesses present considerable security threats, resulting in unauthorized entry and car theft. The suggested approach combines a conventional keyless entry feature with an extra security measure. Implementing multi-factor authentication significantly improves the security of systems that allow keyless entry by reducing the likelihood of unauthorized access. Research shows that the benefits of using two-factor authentication, such as a substantial increase in security, far outweigh any minor drawbacks.
文摘To ensure the access security of 6G,physical-layer authentication(PLA)leverages the randomness and space-time-frequency uniqueness of the channel to provide unique identity signatures for transmitters.Furthermore,the introduction of artificial intelligence(AI)facilitates the learning of the distribution characteristics of channel fingerprints,effectively addressing the uncertainties and unknown dynamic challenges in wireless link modeling.This paper reviews representative AI-enabled PLA schemes and proposes a graph neural network(GNN)-based PLA approach in response to the challenges existing methods face in identifying mobile users.Simulation results demonstrate that the proposed method outperforms six baseline schemes in terms of authentication accuracy.Furthermore,this paper outlines the future development directions of PLA.
基金supported by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology and Natural Science Foundation of Shandong Province,China(Grant no.ZR202111230202).
文摘With the rapid development and widespread adoption of Internet of Things(IoT)technology,the innovative concept of the Internet of Vehicles(IoV)has emerged,ushering in a new era of intelligent transportation.Since vehicles are mobile entities,they move across different domains and need to communicate with the Roadside Unit(RSU)in various regions.However,open environments are highly susceptible to becoming targets for attackers,posing significant risks of malicious attacks.Therefore,it is crucial to design a secure authentication protocol to ensure the security of communication between vehicles and RSUs,particularly in scenarios where vehicles cross domains.In this paper,we propose a provably secure cross-domain authentication and key agreement protocol for IoV.Our protocol comprises two authentication phases:intra-domain authentication and cross-domain authentication.To ensure the security of our protocol,we conducted rigorous analyses based on the ROR(Real-or-Random)model and Scyther.Finally,we show in-depth comparisons of our protocol with existing ones from both security and performance perspectives,fully demonstrating its security and efficiency.
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 the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology and Natural Science Foundation of Shandong Province,China(Grant no.ZR202111230202).
文摘As a model for the next generation of the Internet,the metaverse—a fully immersive,hyper-temporal virtual shared space—is transitioning from imagination to reality.At present,the metaverse has been widely applied in a variety of fields,including education,social entertainment,Internet of vehicles(IoV),healthcare,and virtual tours.In IoVs,researchers primarily focus on using the metaverse to improve the traffic safety of vehicles,while paying limited attention to passengers’social needs.At the same time,Social Internet ofVehicles(SIoV)introduces the concept of social networks in IoV to provide better resources and services for users.However,the problem of single interaction between SIoVand users has become increasingly prominent.In this paper,we first introduce a SIoVenvironment combined with the metaverse.In this environment,we adopt blockchain as the platform of the metaverse to provide a decentralized environment.Concerning passengers’social data may contain sensitive/private information,we then design an authentication and key agreement protocol calledMSIoV-AKAto protect the communications.Through formal security verifications in the real-or-random(ROR)model and using the AVISPA(Automated Validation of Internet Security Protocols and Applications)tool,we firmly verify the security of the protocol.Finally,detailed comparisons are made between our protocol and robust protocols/schemes in terms of computational cost and communication cost.In addition,we implement the MSIoV-AKA protocol in the Ethereum test network and Hyperledger Sawtooth to show the practicality.
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 by the Institute of Information&Communications Technology Planning&Evaluation(1ITP)(Project Nos.RS-2024-00438551,30%,2022-11220701,30%,2021-0-01816,30%)the National Research Foundation of Korea(NRF)grant funded by the Korean Government(Project No.RS2023-00208460,10%).
文摘To date,many previous studies have been proposed for driver authentication;however,these solutions have many shortcomings and are still far from practical for real-world applications.In this paper,we tackle the shortcomings of the existing solutions and reach toward proposing a lightweight and practical authentication system,dubbed DriveMe,for identifying drivers on cars.Our novelty aspects are 1⃝Lightweight scheme that depends only on a single sensor data(i.e.,pressure readings)attached to the driver’s seat and belt.2⃝Practical evaluation in which one-class authentication models are trained from only the owner users and tested using data collected from both owners and attackers.3⃝Rapid Authentication to quickly identify drivers’identities using a few pressure samples collected within short durations(1,2,3,5,or 10 s).4⃝Realistic experiments where the sensory data is collected from real experiments rather than computer simulation tools.We conducted real experiments and collected about 13,200 samples and 22,800 samples of belt-only and seat-only datasets from all 12 users under different settings.To evaluate system effectiveness,we implemented extensive evaluation scenarios using four one-class detectors One-Class Support Vector Machine(OCSVM),Local Outlier Factor(LOF),Isolation Forest(IF),and Elliptic Envelope(EE),three dataset types(belt-only,seat-only,and fusion),and four different dataset sizes.Our average experimental results show that the system can authenticate the driver with an F1 score of 93.1%for seat-based data using OCSVM classifier,an F1 score of 98.53%for fusion-based data using LOF classifier,an F1 score of 91.65%for fusion-based data using IF classifier,and an F1 score of 95.79%for fusion-based data using EE classifier.
基金funding from the Korea Institute for Advancement of Technology(KIAT)through a grant provided by the Korean Government Ministry of Trade,Industry,and Energy(MOTIE)(RS-2024-00415520,Training Industrial Security Specialist for High-Tech Industry)Additional support was received from the Ministry of Science and ICT(MSIT)under the ICAN(ICT Challenge and Advanced Network of HRD)program(No.IITP-2022-RS-2022-00156310)overseen by the Institute of Information&Communication Technology Planning and Evaluation(IITP).
文摘The Internet of Things(IoT)is extensively applied across various industrial domains,such as smart homes,factories,and intelligent transportation,becoming integral to daily life.Establishing robust policies for managing and governing IoT devices is imperative.Secure authentication for IoT devices in resource-constrained environments remains challenging due to the limitations of conventional complex protocols.Prior methodologies enhanced mutual authentication through key exchange protocols or complex operations,which are impractical for lightweight devices.To address this,our study introduces the privacy-preserving software-defined range proof(SDRP)model,which achieves secure authentication with low complexity.SDRP minimizes the overhead of confidentiality and authentication processes by utilizing range proof to verify whether the attribute information of a user falls within a specific range.Since authentication is performed using a digital ID sequence generated from indirect personal data,it can avoid the disclosure of actual individual attributes.Experimental results demonstrate that SDRP significantly improves security efficiency,increasing it by an average of 93.02%compared to conventional methods.It mitigates the trade-off between security and efficiency by reducing leakage risk by an average of 98.7%.
文摘The integration of artificial intelligence(AI)with advanced power technologies is transforming energy system management,particularly through real-time data monitoring and intelligent decision-making driven by Artificial Intelligence Generated Content(AIGC).However,the openness of power system channels and the resource-constrained nature of power sensors have led to new challenges for the secure transmission of power data and decision instructions.Although traditional public key cryptographic primitives can offer high security,the substantial key management and computational overhead associated with these primitives make them unsuitable for power systems.To ensure the real-time and security of power data and command transmission,we propose a lightweight identity authentication scheme tailored for power AIGC systems.The scheme utilizes lightweight symmetric encryption algorithms,minimizing the resource overhead on power sensors.Additionally,it incorporates a dynamic credential update mechanism,which can realize the rotation and update of temporary credentials to ensure anonymity and security.We rigorously validate the security of the scheme using the Real-or-Random(ROR)model and AVISPA simulation,and the results show that our scheme can resist various active and passive attacks.Finally,performance comparisons and NS3 simulation results demonstrate that our proposed scheme offers enhanced security features with lower overhead,making it more suitable for power AIGC systems compared to existing solutions.
基金funded and supported by the UCSI University Research Excellence&Innovation Grant(REIG),REIG-ICSDI-2024/044.
文摘In the rapidly evolving landscape of intelligent transportation systems,the security and authenticity of vehicular communication have emerged as critical challenges.As vehicles become increasingly interconnected,the need for robust authentication mechanisms to safeguard against cyber threats and ensure trust in an autonomous ecosystem becomes essential.On the other hand,using intelligence in the authentication system is a significant attraction.While existing surveys broadly address vehicular security,a critical gap remains in the systematic exploration of Deep Learning(DL)-based authentication methods tailored to these communication paradigms.This survey fills that gap by offering a comprehensive analysis of DL techniques—including supervised,unsupervised,reinforcement,and hybrid learning—for vehicular authentication.This survey highlights novel contributions,such as a taxonomy of DL-driven authentication protocols,real-world case studies,and a critical evaluation of scalability and privacy-preserving techniques.Additionally,this paper identifies unresolved challenges,such as adversarial resilience and real-time processing constraints,and proposes actionable future directions,including lightweight model optimization and blockchain integration.By grounding the discussion in concrete applications,such as biometric authentication for driver safety and adaptive key management for infrastructure security,this survey bridges theoretical advancements with practical deployment needs,offering a roadmap for next-generation secure intelligent vehicular ecosystems for the modern world.
基金supported by Science Foundation of China University of Petroleum,Beijing(No.2462023QNXZ010,No.2462023XKBH012,No.2462024XKBH009)China Postdoctoral Science Foundation(No.2024M753612,No.GZC20233101).
文摘The northern structural belt of Kuqa Depression is adjacent to the South Tianshan orogenic belt, which are characterized by complex geological conditions. The reservoir quality of the Jurassic Ahe Formation is controlled by sedimentation, diagenesis, and tectonics, and show complex pore structure and strong heterogeneity, thereby hindering effective natural gas exploration and development. Core, thin sections, cathodoluminescence (CL), scanning electron microscopy (SEM), conventional well logs and image logs are used to characterize the petrological characteristics and pore systems. Then a comprehensive analysis integrating sedimentation, diagenesis, and tectonics is performed to unravel the reservoir formation mechanism and distribution of reservoir quality. Results show that reservoir properties are generally environmentally selective. Coarse grained sandbodies (gravelly sandstones) formed in high depositional-energy have the best physical properties, while fine sandstone and mudstone with low depositional energy is easily to be tightly compacted, and have poor reservoir quality. Porosity usually decreases with compaction and cementation, and increases due to dissolution. Clay minerals filling pores result in a deterioration of the pore structure. Microfracture formed by fracturing can connect the matrix pores, effectively improving the reservoirs’ permeability. The differential distribution of fractures and in-situ stress plays an important role in modifying reservoir quality. The in-situ stress has obvious control over the matrix physical properties and fracture effectiveness. The matrix physical properties are negatively correlated with the value of horizontal stress difference (Δσ). As the value of Δσ increases, the pore structure becomes more complex, and the macroscopic reservoir quality becomes poor. The smaller the strike divergence between the natural fracture and SHmax, the lower the value of Δσ in the fracture layers is, and the better the fracture effectiveness is. Under the control of ternary factors on the reservoir, sedimentation-diagenesis jointly affect the matrix reservoir quality, while fractures and in-situ stress caused by tectonism affect the permeability and hydrocarbon productivity of the reservoir. Affected by ternary factors, reservoir quality and hydrocarbon productivity show obvious differences within the various structural location. Reservoir quality in tight sandstones can be predicted by integrating sedimentation, diagenesis, and tectonics (fracture and in-situ stress) in a compressional tectonic setting like Kuqa Depression. The research results will provide insights into the efficient exploration of oil and gas in Kuqa Depression as well as similar compressional tectonic settings elsewhere.
基金supported in part by the National Key R&D Program of China under grant no.2022YFB2703000in part by the Young Backbone Teachers Support Plan of BISTU under grant no.YBT202437+1 种基金in part by the R&D Program of Beijing Municipal Education Commission under grant no.KM202211232012in part by the Educational Innovation Program of BISTU under grant no.2025JGYB19。
文摘Physical layer authentication(PLA)in the context of the Internet of Things(IoT)has gained significant attention.Compared with traditional encryption and blockchain technologies,PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself.Some existing PLA solutions rely on static mechanisms,which are insufficient to address the authentication challenges in fifth generation(5G)and beyond wireless networks.Additionally,with the massive increase in mobile device access,the communication security of the IoT is vulnerable to spoofing attacks.To overcome the above challenges,this paper proposes a lightweight deep convolutional neural network(CNN)equipped with squeeze and excitation module(SE module)in dynamic wireless environments,namely SE-ConvNet.To be more specific,a convolution factorization is developed to reduce the complexity of PLA models based on deep learning.Moreover,an SE module is designed in the deep CNN to enhance useful features andmaximize authentication accuracy.Compared with the existing solutions,the proposed SE-ConvNet enabled PLA scheme performs excellently in mobile and time-varying wireless environments while maintaining lower computational complexity.
