Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,B...Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,Bukyiende Subcounty in Uganda where he has been cultivating plantain,coffee and Irish potatoes for the past 16 years.展开更多
The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.De...The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.Despite their transformative impact in fields such as machine translation and intelligent dialogue systems,LLMs face significant challenges.These challenges include safety,security,and privacy concerns that undermine their trustworthiness and effectiveness,such as hallucinations,backdoor attacks,and privacy leakage.Previous works often conflated safety issues with security concerns.In contrast,our study provides clearer and more reasonable definitions for safety,security,and privacy within the context of LLMs.Building on these definitions,we provide a comprehensive overview of the vulnerabilities and defense mechanisms related to safety,security,and privacy in LLMs.Additionally,we explore the unique research challenges posed by LLMs and suggest potential avenues for future research,aiming to enhance the robustness and reliability of LLMs in the face of emerging threats.展开更多
Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, ...Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.展开更多
ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential sec...ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future directions.Through this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.展开更多
The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by phy...The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by physical attacks,EMP(electromagnetic pulse)events,or cyberattacks,such disruptions could cripple essential services like water supply,healthcare,communication,and transportation.Research indicates that an attack on just nine key substations could result in a coast-to-coast blackout lasting up to 18 months,leading to economic collapse,civil unrest,and a breakdown of public order.This paper explores the key vulnerabilities of the grid,the potential impacts of prolonged blackouts,and the role of AI(artificial intelligence)and ML(machine learning)in mitigating these threats.AI-driven cybersecurity measures,predictive maintenance,automated threat response,and EMP resilience strategies are discussed as essential solutions to bolster grid security.Policy recommendations emphasize the need for hardened infrastructure,enhanced cybersecurity,redundant power systems,and AI-based grid management to ensure national resilience.Without proactive measures,the nation remains exposed to a catastrophic power grid failure that could have dire consequences for society and the economy.展开更多
There is a growing recognition of the critical role of security governance in advancing democratic transition in the post-conflict environment.Despite such a recognition,the security sector reform concept has overshad...There is a growing recognition of the critical role of security governance in advancing democratic transition in the post-conflict environment.Despite such a recognition,the security sector reform concept has overshadowed the importance of the overarching strategic role of security governance in transition to democracy,particularly in Africa.This paper assesses the status and challenges facing security governance and how they thwarted the efforts to furthering the democratic transition in South Sudan.The paper shows a deterioration in security,safety and security governance outcomes since the independence of South Sudan in 2011 with such a trend unlikely to be abated in the near future without strategic interventions.Some of the challenges facing security governance in South Sudan include the legacies of some historical events including the“Big Tent Policy”,absence of strategic leadership,lack of overarching policy framework,impractical and tenuous security arrangements in the 2018 peace agreement,persistent postponement of the first elections,and dysfunctional justice sector.The paper provides some strategic and operational recommendations to improve security governance and advance democratic transition in South Sudan.These recommendations include formulation of an inclusive and people-centered national security policy,rigorous judicial reform,and early political agreement on new political infrastructure if conditions for holding the first national elections are not met in 2026.展开更多
The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,dist...The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,distributed renewable resources,and data-driven intelligence,have emerged as the backbone of this evolution.This convergence,however,introduces unprecedented complexities in resilience,security,stability,and market operation.This special issue presents five pivotal studies addressing these interconnected challenges,offering novel methodologies and insights to advance the efficiency,resilience,and sustainability of modern power systems.展开更多
The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facili...The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure.展开更多
Fast and accurate transient stability analysis is crucial to power system operation.With high penetration level of wind power resources,practical dynamic security region(PDSR)with hyper plane expression has outstandin...Fast and accurate transient stability analysis is crucial to power system operation.With high penetration level of wind power resources,practical dynamic security region(PDSR)with hyper plane expression has outstanding advantages in situational awareness and series of optimization problems.The precondition of obtaining accurate PDSR boundary is to locate sufficient points around the boundary(critical points).Therefore,this paper proposes a space division and Wasserstein generative adversarial network with gra-dient penalty(WGAN-GP)based fast generation method of PDSR boundary.First,the typical differential topological characterizations of dynamic security region(DSR)provide strong theoretical foundation that the interior of DSR is hole-free and the boundaries of DSR are tight and knot-free.Then,the space division method is proposed to calculate critical operation area where the PDSR boundary is located,tremen-dously compressing the search space to locate critical points and improving the confidence level of boundary fitting result.Furthermore,the WGAN-GP model is utilized to fast obtain large number of criti-cal points based on learning the data distribution of the small training set aforementioned.Finally,the PDSR boundary with hyperplanes is fitted by the least square method.The case study is tested on the Institute of Electrical and Electronics Engineers(IEEE)39-bus system and the results verify the accuracy and efficiency of the proposed method.展开更多
Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities f...Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented knowledge.To ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and availability.Among these approaches,Intrusion Detection Systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of threats.This study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification algorithm.By combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of attack.Our analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT environments.Experimental results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack detection.Despite the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of environments.Future work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.展开更多
This work focuses on maximizing the minimum user’s security energy efficiency(SEE)in an unmanned aerial vehicle-mounted reconfigurable intelligent surface(UAV-RIS)enhanced short-packet communication(SPC)system.The ba...This work focuses on maximizing the minimum user’s security energy efficiency(SEE)in an unmanned aerial vehicle-mounted reconfigurable intelligent surface(UAV-RIS)enhanced short-packet communication(SPC)system.The base station(BS)provides short packet services to ground users using the non-orthogonal multiple access(NOMA)protocol through UAV-RIS,while preventing eavesdropper attacks.To optimize SEE,a joint optimization is performed concerning power allocation,UAV position,decoding order,and RIS phase shifts.An iterative algorithm based on block coordinate descent is proposed for mixed-integer non-convex SEE optimization problem.The original problem is decomposed into three sub-problems,solved alternately using successive convex approximation(SCA),quadratic transformation,penalty function,and semi-definite programming(SDP).Simulation results demonstrate the performance of the UAV-RIS-enhanced short-packet system under different parameters and verify the algorithm’s convergence.Compared to benchmark schemes such as orthogonal multiple access,long packet communication,and sum SEE,the proposed UAV-RIS-enhanced short-packet scheme achieves the higher minimum user’s SEE.展开更多
This study focuses on urgent research on restoring and enhancing carbon storage capacity in the Beibu Gulf Urban Agglomer-ation of China,a key area in the‘Belt and Road’Initiative,which aligns with carbon peaking an...This study focuses on urgent research on restoring and enhancing carbon storage capacity in the Beibu Gulf Urban Agglomer-ation of China,a key area in the‘Belt and Road’Initiative,which aligns with carbon peaking and neutrality goals.This research ana-lyzes the spatial characteristics of carbon metabolism from 2000 to 2020 and uses models to identify stable carbon sink areas,positive carbon flow corridors,and carbon sequestration nodes.The goal is to construct a carbon metabolism spatial security pattern(CMSSP)and propose territorial ecological restoration strategies under different development demand scenarios.The results show the following:1)in 2020,the study area’s carbon sink decreased by 8.29×10^(4) t C/yr compared with that in 2010 and by 10.83×10^(4) t C/yr compared with that in 2000.High-carbon sinks were found mainly in mountainous areas,whereas low-carbon sinks are concentrated in urban con-struction land,rural residential areas,and land margins.2)From 2000 to 2020,the spatial security pattern of carbon metabolism tended to be‘high in the middle of the east and west and low in the gulf.’In 2000,2010,and 2020,16 stable carbon sinks were identified.The carbon energy flow density in Guangxi was greater than that in Guangdong and Hainan,with positive carbon flow corridors located primarily in Guangxi and Guangdong.The number of carbon sequestration nodes remained stable at approximately 15,mainly in Guangxi and Hainan.3)Scenario simulations revealed that under the Nature-based mild restoration scenario,the carbon sink rate will reach 611.85×10^(4) t C/yr by 2030 and increase to 612.45×10^(4) t C/yr by 2060,with stable carbon sinks increasing to 18.In the restora-tion scenario based on Anti-globalization,the carbon sink will decrease from 610.24×10^(4) t C/yr in 2030 to 605.19×10^(4) t C/yr in 2060,with the disappearance of some positive carbon flow corridors and stable carbon sinks.Under the Human-based sustainable restoration scenario,the carbon sink area will decrease from 607.00×10^(4) t C/yr in 2030 to 596.39×10^(4) t C/yr in 2060,with carbon sink areas frag-menting and positive carbon flow corridors becoming less dense.4)On the basis of the current and predicted CMSSPs,this study ex-plores spatial ecological restoration strategies for high-carbon storage areas in bay urban agglomerations at four levels:the land control region,urban agglomeration structure system,carbon sink structure and bay structure control region.展开更多
Software-related security aspects are a growing and legitimate concern,especially with 5G data available just at our palms.To conduct research in this field,periodic comparative analysis is needed with the new techniq...Software-related security aspects are a growing and legitimate concern,especially with 5G data available just at our palms.To conduct research in this field,periodic comparative analysis is needed with the new techniques coming up rapidly.The purpose of this study is to review the recent developments in the field of security integration in the software development lifecycle(SDLC)by analyzing the articles published in the last two decades and to propose a way forward.This review follows Kitchenham’s review protocol.The review has been divided into three main stages including planning,execution,and analysis.From the selected 100 articles,it becomes evident that need of a collaborative approach is necessary for addressing critical software security risks(CSSRs)through effective risk management/estimation techniques.Quantifying risks using a numeric scale enables a comprehensive understanding of their severity,facilitating focused resource allocation and mitigation efforts.Through a comprehensive understanding of potential vulnerabilities and proactive mitigation efforts facilitated by protection poker,organizations can prioritize resources effectively to ensure the successful outcome of projects and initiatives in today’s dynamic threat landscape.The review reveals that threat analysis and security testing are needed to develop automated tools for the future.Accurate estimation of effort required to prioritize potential security risks is a big challenge in software security.The accuracy of effort estimation can be further improved by exploring new techniques,particularly those involving deep learning.It is also imperative to validate these effort estimation methods to ensure all potential security threats are addressed.Another challenge is selecting the right model for each specific security threat.To achieve a comprehensive evaluation,researchers should use well-known benchmark checklists.展开更多
This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain an...This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical content.The participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security weaknesses.This multi faceted exploration led to an indepth evaluation of the model’s performance and security.Notably,the correlation between accuracy,detection rate,and error rate was scrutinized.This analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance variability.The study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications.Applying blockchain to generative AI-created medical content addresses key personal information protection issues.By utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical images.This approach not only enhances security but also enables transparent and tamperproof record-keeping.Additionally,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient privacy.In conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient privacy.The proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content management.Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems,and visual diagnostic systems.展开更多
The simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)can independently adjust surface’s reflection and transmission coefficients so as to enhance space coverage.For a multiple-inpu...The simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)can independently adjust surface’s reflection and transmission coefficients so as to enhance space coverage.For a multiple-input multiple-output(MIMO)communication system with a STAR-RIS,a base station(BS),an eavesdropper,and multiple users,the system security rate is studied.A joint design of the power allocation at the transmitter and phase shift matrices for reflection and transmission at the STAR-RIS is conducted,in order to maximize the worst achievable security data rate(ASDR).Since the problem is nonconvex and hence challenging,a particle swarm optimization(PSO)based algorithm is developed to tackle the problem.Both the cases of continuous and discrete phase shift matrices at the STAR-RIS are considered.Simulation results demonstrate the effectiveness of the proposed algorithm and shows the benefits of using STAR-RIS in MIMO mutliuser systems.展开更多
文摘Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,Bukyiende Subcounty in Uganda where he has been cultivating plantain,coffee and Irish potatoes for the past 16 years.
基金supported by the National Key R&D Program of China under Grant No.2022YFB3103500the National Natural Science Foundation of China under Grants No.62402087 and No.62020106013+3 种基金the Sichuan Science and Technology Program under Grant No.2023ZYD0142the Chengdu Science and Technology Program under Grant No.2023-XT00-00002-GXthe Fundamental Research Funds for Chinese Central Universities under Grants No.ZYGX2020ZB027 and No.Y030232063003002the Postdoctoral Innovation Talents Support Program under Grant No.BX20230060.
文摘The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.Despite their transformative impact in fields such as machine translation and intelligent dialogue systems,LLMs face significant challenges.These challenges include safety,security,and privacy concerns that undermine their trustworthiness and effectiveness,such as hallucinations,backdoor attacks,and privacy leakage.Previous works often conflated safety issues with security concerns.In contrast,our study provides clearer and more reasonable definitions for safety,security,and privacy within the context of LLMs.Building on these definitions,we provide a comprehensive overview of the vulnerabilities and defense mechanisms related to safety,security,and privacy in LLMs.Additionally,we explore the unique research challenges posed by LLMs and suggest potential avenues for future research,aiming to enhance the robustness and reliability of LLMs in the face of emerging threats.
文摘Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.
文摘ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future directions.Through this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.
文摘The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by physical attacks,EMP(electromagnetic pulse)events,or cyberattacks,such disruptions could cripple essential services like water supply,healthcare,communication,and transportation.Research indicates that an attack on just nine key substations could result in a coast-to-coast blackout lasting up to 18 months,leading to economic collapse,civil unrest,and a breakdown of public order.This paper explores the key vulnerabilities of the grid,the potential impacts of prolonged blackouts,and the role of AI(artificial intelligence)and ML(machine learning)in mitigating these threats.AI-driven cybersecurity measures,predictive maintenance,automated threat response,and EMP resilience strategies are discussed as essential solutions to bolster grid security.Policy recommendations emphasize the need for hardened infrastructure,enhanced cybersecurity,redundant power systems,and AI-based grid management to ensure national resilience.Without proactive measures,the nation remains exposed to a catastrophic power grid failure that could have dire consequences for society and the economy.
文摘There is a growing recognition of the critical role of security governance in advancing democratic transition in the post-conflict environment.Despite such a recognition,the security sector reform concept has overshadowed the importance of the overarching strategic role of security governance in transition to democracy,particularly in Africa.This paper assesses the status and challenges facing security governance and how they thwarted the efforts to furthering the democratic transition in South Sudan.The paper shows a deterioration in security,safety and security governance outcomes since the independence of South Sudan in 2011 with such a trend unlikely to be abated in the near future without strategic interventions.Some of the challenges facing security governance in South Sudan include the legacies of some historical events including the“Big Tent Policy”,absence of strategic leadership,lack of overarching policy framework,impractical and tenuous security arrangements in the 2018 peace agreement,persistent postponement of the first elections,and dysfunctional justice sector.The paper provides some strategic and operational recommendations to improve security governance and advance democratic transition in South Sudan.These recommendations include formulation of an inclusive and people-centered national security policy,rigorous judicial reform,and early political agreement on new political infrastructure if conditions for holding the first national elections are not met in 2026.
文摘The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,distributed renewable resources,and data-driven intelligence,have emerged as the backbone of this evolution.This convergence,however,introduces unprecedented complexities in resilience,security,stability,and market operation.This special issue presents five pivotal studies addressing these interconnected challenges,offering novel methodologies and insights to advance the efficiency,resilience,and sustainability of modern power systems.
文摘The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure.
基金funded in part by the National Key Research and Development Program of China(2020YFB0905900)in part by Science and Technology Project of State Grid Corporation of China(SGCC)The Key Technologies for Electric Internet of Things(SGTJDK00DWJS2100223).
文摘Fast and accurate transient stability analysis is crucial to power system operation.With high penetration level of wind power resources,practical dynamic security region(PDSR)with hyper plane expression has outstanding advantages in situational awareness and series of optimization problems.The precondition of obtaining accurate PDSR boundary is to locate sufficient points around the boundary(critical points).Therefore,this paper proposes a space division and Wasserstein generative adversarial network with gra-dient penalty(WGAN-GP)based fast generation method of PDSR boundary.First,the typical differential topological characterizations of dynamic security region(DSR)provide strong theoretical foundation that the interior of DSR is hole-free and the boundaries of DSR are tight and knot-free.Then,the space division method is proposed to calculate critical operation area where the PDSR boundary is located,tremen-dously compressing the search space to locate critical points and improving the confidence level of boundary fitting result.Furthermore,the WGAN-GP model is utilized to fast obtain large number of criti-cal points based on learning the data distribution of the small training set aforementioned.Finally,the PDSR boundary with hyperplanes is fitted by the least square method.The case study is tested on the Institute of Electrical and Electronics Engineers(IEEE)39-bus system and the results verify the accuracy and efficiency of the proposed method.
文摘Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented knowledge.To ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and availability.Among these approaches,Intrusion Detection Systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of threats.This study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification algorithm.By combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of attack.Our analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT environments.Experimental results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack detection.Despite the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of environments.Future work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.
基金co-supported by the National Natural Science Foundation of China(Nos.U23A20279,62271094)the National Key R&D Program of China(No.SQ2023YFB2500024)+2 种基金the Science Foundation for Youths of Natural Science Foundation of Sichuan Provincial,China(No.2022NSFSC0936)the China Postdoctoral Science Foundation(No.2022M720666)the Open Fund of Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications,China(No.BDIC-2023-B-002).
文摘This work focuses on maximizing the minimum user’s security energy efficiency(SEE)in an unmanned aerial vehicle-mounted reconfigurable intelligent surface(UAV-RIS)enhanced short-packet communication(SPC)system.The base station(BS)provides short packet services to ground users using the non-orthogonal multiple access(NOMA)protocol through UAV-RIS,while preventing eavesdropper attacks.To optimize SEE,a joint optimization is performed concerning power allocation,UAV position,decoding order,and RIS phase shifts.An iterative algorithm based on block coordinate descent is proposed for mixed-integer non-convex SEE optimization problem.The original problem is decomposed into three sub-problems,solved alternately using successive convex approximation(SCA),quadratic transformation,penalty function,and semi-definite programming(SDP).Simulation results demonstrate the performance of the UAV-RIS-enhanced short-packet system under different parameters and verify the algorithm’s convergence.Compared to benchmark schemes such as orthogonal multiple access,long packet communication,and sum SEE,the proposed UAV-RIS-enhanced short-packet scheme achieves the higher minimum user’s SEE.
基金Under the auspices of the National Natural Science Foundation of China(No.52268008)。
文摘This study focuses on urgent research on restoring and enhancing carbon storage capacity in the Beibu Gulf Urban Agglomer-ation of China,a key area in the‘Belt and Road’Initiative,which aligns with carbon peaking and neutrality goals.This research ana-lyzes the spatial characteristics of carbon metabolism from 2000 to 2020 and uses models to identify stable carbon sink areas,positive carbon flow corridors,and carbon sequestration nodes.The goal is to construct a carbon metabolism spatial security pattern(CMSSP)and propose territorial ecological restoration strategies under different development demand scenarios.The results show the following:1)in 2020,the study area’s carbon sink decreased by 8.29×10^(4) t C/yr compared with that in 2010 and by 10.83×10^(4) t C/yr compared with that in 2000.High-carbon sinks were found mainly in mountainous areas,whereas low-carbon sinks are concentrated in urban con-struction land,rural residential areas,and land margins.2)From 2000 to 2020,the spatial security pattern of carbon metabolism tended to be‘high in the middle of the east and west and low in the gulf.’In 2000,2010,and 2020,16 stable carbon sinks were identified.The carbon energy flow density in Guangxi was greater than that in Guangdong and Hainan,with positive carbon flow corridors located primarily in Guangxi and Guangdong.The number of carbon sequestration nodes remained stable at approximately 15,mainly in Guangxi and Hainan.3)Scenario simulations revealed that under the Nature-based mild restoration scenario,the carbon sink rate will reach 611.85×10^(4) t C/yr by 2030 and increase to 612.45×10^(4) t C/yr by 2060,with stable carbon sinks increasing to 18.In the restora-tion scenario based on Anti-globalization,the carbon sink will decrease from 610.24×10^(4) t C/yr in 2030 to 605.19×10^(4) t C/yr in 2060,with the disappearance of some positive carbon flow corridors and stable carbon sinks.Under the Human-based sustainable restoration scenario,the carbon sink area will decrease from 607.00×10^(4) t C/yr in 2030 to 596.39×10^(4) t C/yr in 2060,with carbon sink areas frag-menting and positive carbon flow corridors becoming less dense.4)On the basis of the current and predicted CMSSPs,this study ex-plores spatial ecological restoration strategies for high-carbon storage areas in bay urban agglomerations at four levels:the land control region,urban agglomeration structure system,carbon sink structure and bay structure control region.
文摘Software-related security aspects are a growing and legitimate concern,especially with 5G data available just at our palms.To conduct research in this field,periodic comparative analysis is needed with the new techniques coming up rapidly.The purpose of this study is to review the recent developments in the field of security integration in the software development lifecycle(SDLC)by analyzing the articles published in the last two decades and to propose a way forward.This review follows Kitchenham’s review protocol.The review has been divided into three main stages including planning,execution,and analysis.From the selected 100 articles,it becomes evident that need of a collaborative approach is necessary for addressing critical software security risks(CSSRs)through effective risk management/estimation techniques.Quantifying risks using a numeric scale enables a comprehensive understanding of their severity,facilitating focused resource allocation and mitigation efforts.Through a comprehensive understanding of potential vulnerabilities and proactive mitigation efforts facilitated by protection poker,organizations can prioritize resources effectively to ensure the successful outcome of projects and initiatives in today’s dynamic threat landscape.The review reveals that threat analysis and security testing are needed to develop automated tools for the future.Accurate estimation of effort required to prioritize potential security risks is a big challenge in software security.The accuracy of effort estimation can be further improved by exploring new techniques,particularly those involving deep learning.It is also imperative to validate these effort estimation methods to ensure all potential security threats are addressed.Another challenge is selecting the right model for each specific security threat.To achieve a comprehensive evaluation,researchers should use well-known benchmark checklists.
文摘This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical content.The participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security weaknesses.This multi faceted exploration led to an indepth evaluation of the model’s performance and security.Notably,the correlation between accuracy,detection rate,and error rate was scrutinized.This analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance variability.The study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications.Applying blockchain to generative AI-created medical content addresses key personal information protection issues.By utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical images.This approach not only enhances security but also enables transparent and tamperproof record-keeping.Additionally,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient privacy.In conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient privacy.The proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content management.Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems,and visual diagnostic systems.
文摘The simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)can independently adjust surface’s reflection and transmission coefficients so as to enhance space coverage.For a multiple-input multiple-output(MIMO)communication system with a STAR-RIS,a base station(BS),an eavesdropper,and multiple users,the system security rate is studied.A joint design of the power allocation at the transmitter and phase shift matrices for reflection and transmission at the STAR-RIS is conducted,in order to maximize the worst achievable security data rate(ASDR).Since the problem is nonconvex and hence challenging,a particle swarm optimization(PSO)based algorithm is developed to tackle the problem.Both the cases of continuous and discrete phase shift matrices at the STAR-RIS are considered.Simulation results demonstrate the effectiveness of the proposed algorithm and shows the benefits of using STAR-RIS in MIMO mutliuser systems.
文摘近日,武汉大学国家网络安全学院2023级硕士研究生闫楠作为第一作者撰写的论文被第34届USENIX安全研讨会(The34th USENIX Security Symposium 2025)录用。论文题目为“Embed X:Embedding-Based Cross-Trigger Backdoor Attack Against Large Language Models”(《Embed X:基于嵌入的跨触发器大语言模型后门攻击》),指导老师为国家网络安全学院副研究员李雨晴(通信作者)、教授陈晶(通信作者)、副教授何琨。华中科技大学副教授王雄、香港科技大学教授李波参与合作。
