Aiming at the issues of privacy security in Internet of Things （IoT） applications, we propose an effective risk assessment model to handle probabilistic causality of evaluation factors and derive weights of influenc...Aiming at the issues of privacy security in Internet of Things （IoT） applications, we propose an effective risk assessment model to handle probabilistic causality of evaluation factors and derive weights of influence-relation of propagation paths. The model undertakes probabilistic inference and generates values of risk probability for assets and propagation paths by using Bayesian causal relation-network and prior probability. According to Bayes- ian network （BN） structure, the risk analysts can easily find out relevant risk propagation paths and calculate weight values of each path by using decision-making trial and evaluation laboratory （DEMATEL）. This model is applied to determine the risk level of assets and each risk propagation path as well as implement countermeasure of recommendation in accordance with evaluation results. The simulation analysis shows that this model efficiently revises recommendation of countermeasures for decision-makers and mitigates risk to an acceptable range, in addition, it provides the theoretical basis for decision-making of privacy security risk assessment （PSRA） for further development in lot area.展开更多
Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data.However,there is still a potential risk of privacy leakage,for example,attackers can obta...Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data.However,there is still a potential risk of privacy leakage,for example,attackers can obtain the original data through model inference attacks.Therefore,safeguarding the privacy of model parameters becomes crucial.One proposed solution involves incorporating homomorphic encryption algorithms into the federated learning process.However,the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal nodes.To solve the above problems,this paper proposes a privacy protection scheme named Federated Learning-Elastic Averaging Stochastic Gradient Descent(FL-EASGD)based on a fully homomorphic encryption algorithm.First,this paper introduces the homomorphic encryption algorithm into the FL-EASGD scheme to preventmodel plaintext leakage and realize privacy security in the process ofmodel aggregation.Second,this paper designs a robust model aggregation algorithm by adding time variables and constraint coefficients,which ensures the accuracy of model prediction while solving performance differences such as computation speed and node anomalies such as downtime of each participant.In addition,the scheme in this paper preserves the independent exploration of the local model by the nodes of each party,making the model more applicable to the local data distribution.Finally,experimental analysis shows that when there are abnormalities in the participants,the efficiency and accuracy of the whole protocol are not significantly affected.展开更多
As the 5G architecture gains momentum,interest in 6G is growing.The proliferation of Internet of Things(IoT)devices,capable of capturing sensitive images,has increased the need for secure transmission and robust acces...As the 5G architecture gains momentum,interest in 6G is growing.The proliferation of Internet of Things(IoT)devices,capable of capturing sensitive images,has increased the need for secure transmission and robust access control mechanisms.The vast amount of data generated by low-computing devices poses a challenge to traditional centralized access control,which relies on trusted third parties and complex computations,resulting in intricate interactions,higher hardware costs,and processing delays.To address these issues,this paper introduces a novel distributed access control approach that integrates a decentralized and lightweight encryption mechanism with image transmission.This method enhances data security and resource efficiency without imposing heavy computational and network burdens.In comparison to the best existing approach,it achieves a 7%improvement in accuracy,effectively addressing existing gaps in lightweight encryption and recognition performance.展开更多
The Metaverse is the digitization of the real world,supported by big data,AI,5G,cloud computing,blockchain,encryption algorithm,perception technology,digital twin,virtual engine,and other technologies that interact wi...The Metaverse is the digitization of the real world,supported by big data,AI,5G,cloud computing,blockchain,encryption algorithm,perception technology,digital twin,virtual engine,and other technologies that interact with human behavior and thoughts in avatars through digital identity.Cracking the trust problem brought by the avatar depends on the privacy security and authentication technology for individuals using digital identities to enter the Metaverse.To accomplish personal domination of the avatar,metaverse users need privacy data feeding and emotion projection.They must be equipped with proprietary algorithms to process and analyze the complex data generated in adaptive interactions,which challenges the privacy security of user data in the Metaverse.Distinguishing the significance of different identifiers in personal identity generation while imposing different behavioral regulatory requirements on data processing levels may better balance the relationship between personal privacy security and digital identity protection and data utilization in the Metaverse.In response to digital identity issues,there is an objective need to establish a unified digital identity authentication system to gain the general trust of society.Further,the remedies for a right to personality can be applied to the scenario of unlawful infringement of digital identity and privacy security.展开更多
While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contrad...While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. In this paper, we firstly reviewed the enormous benefits and challenges of security and privacy in Big Data. Then, we present some possible methods and techniques to ensure Big Data security and privacy.展开更多
The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different fields.Physical settings are probed for data,which is then transferred via linked networks.There ...The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different fields.Physical settings are probed for data,which is then transferred via linked networks.There are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny sensors.When it comes to deploying IoT,everyone agrees that security is the biggest issue.This is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square footage.This research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted circumstances.Using lightweight cryptography,the information sent between these gadgets may be secured.In order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance features.We also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.展开更多
Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the...Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.展开更多
The study of vehicular ad-hoc networks(VANETs)has received significant attention among academia;even so,its security and privacy still become a central issue that is wide-open to discuss.The authentication schemes dep...The study of vehicular ad-hoc networks(VANETs)has received significant attention among academia;even so,its security and privacy still become a central issue that is wide-open to discuss.The authentication schemes deployed in VANETs have a substantial impact on its security and privacy.Many researchers have proposed a variety of schemes related to the information verification and efficiency improvement in VANETs.In recent years,many papers have proposed identity-based batch verification(IBV)schemes in regard to diminishing overhead in the message verification process in VANETs.This survey begins with providing background information about VANETs and clarifying its security and privacy,as well as performance requirements that must be satisfied.After presenting an outlook of some relevant surveys of VANETs,a brief review of some IBV schemes published in recent years is conferred.The detailed approach of each scheme,with a comprehensive comparison between them,has been provided afterward.Finally,we summarize those recent studies and possible future improvements.展开更多
Modern communication allows billions of objects in the physical world as well as virtual environments to exchange data with each other in an autonomous way so as to create smart environments. However, modern communica...Modern communication allows billions of objects in the physical world as well as virtual environments to exchange data with each other in an autonomous way so as to create smart environments. However, modern communication also introduces new challenges for the security of systems and processes and the privacy of individuals. There is an increasing demand for development of new security and privacy approaches to guarantee the security, privacy, integ- rity, and availability of resources in modern communication.展开更多
The Personal Information Protection Law,as the first law on personal information protection in China,hits the people’s most concerned,realistic and direct privacy and information security issues,and plays an extremel...The Personal Information Protection Law,as the first law on personal information protection in China,hits the people’s most concerned,realistic and direct privacy and information security issues,and plays an extremely important role in promoting the development of the digital economy,the legalization of socialism with Chinese characteristics and social public security,and marks a new historical development stage in the protection of personal information in China.However,the awareness of privacy protection and privacy protection behavior of the public in personal information privacy protection is weak.Based on the literature review and in-depth understanding of current legal regulations,this study integrates the relevant literature and theoretical knowledge of the Personal Protection Law to construct a conceptual model of“privacy information protection willingness-privacy information protection behavior”.Taking the residents of Foshan City as an example,this paper conducts a questionnaire survey on their attitudes toward the Personal Protection Law,analyzes the factors influencing their willingness to protect their privacy and their behaviors,and explores the mechanisms of their influencing variables,to provide advice and suggestions for promoting the protection of privacy information and building a security barrier for the high-quality development of public information security.展开更多
The fifth generation(5G)system is the forthcoming generation of the mobile communication system.It has numerous additional features and offers an extensively high data rate,more capacity,and low latency.However,these ...The fifth generation(5G)system is the forthcoming generation of the mobile communication system.It has numerous additional features and offers an extensively high data rate,more capacity,and low latency.However,these features and applications have many problems and issues in terms of security,which has become a great challenge in the telecommunication industry.This paper aimed to propose a solution to preserve the user identity privacy in the 5G system that can identify permanent identity by using Variable Mobile Subscriber Identity,which randomly changes and does not use the permanent identity between the user equipment and home network.Through this mechanism,the user identity privacy would be secured and hidden.Moreover,it improves the synchronization between mobile users and home networks.Additionally,its compliance with the Authentication and Key Agreement(AKA)structure was adopted in the previous generations.It can be deployed efficiently in the preceding generations because the current architecture imposes minimal modifications on the network parties without changes in the authentication vector’s message size.Moreover,the addition of any hardware to the AKA carries minor adjustments on the network parties.In this paper,the ProVerif is used to verify the proposed scheme.展开更多
Big data has been taken as a Chinese national strategy in order to satisfy the developments of the social and economic requirements and the development of new information technology. The prosperity of big data brings ...Big data has been taken as a Chinese national strategy in order to satisfy the developments of the social and economic requirements and the development of new information technology. The prosperity of big data brings not only convenience to people's daily life and more opportunities to enterprises, but more challenges with information security as well. This paper has a research on new types and features of information security issues in the age of big data, and puts forward the solutions for the above issues: build up the big data security management platform, set up the establishment of information security system and implement relevant laws and regulations.展开更多
The ever-increasing needs of Internet of Things networks (IoTn) present considerable issues in computing complexity, security, trust, and authentication, among others. This gets increasingly more challenging as techno...The ever-increasing needs of Internet of Things networks (IoTn) present considerable issues in computing complexity, security, trust, and authentication, among others. This gets increasingly more challenging as technology advances, and its use expands. As a consequence, boosting the capacity of these networks has garnered widespread attention. As a result, 5G, the next phase of cellular networks, is expected to be a game-changer, bringing with it faster data transmission rates, more capacity, improved service quality, and reduced latency. However, 5G networks continue to confront difficulties in establishing pervasive and dependable connections amongst high-speed IoT devices. Thus, to address the shortcomings in current recommendations, we present a unified architecture based on software-defined networks (SDNs) that provides 5G-enabled devices that must have complete secrecy. Through SDN, the architecture streamlines network administration while optimizing network communications. A mutual authentication protocol using elliptic curve cryptography is introduced for mutual authentication across certificate authorities and clustered heads in IoT network deployments based on IoT. Again, a dimensionality reduction intrusion detection mechanism is introduced to decrease computational cost and identify possible network breaches. However, to leverage the method’s potential, the initial module's security is reviewed. The second module is evaluated and compared to modern models.展开更多
In a database-as-a-service(DaaS)model,a data owner stores data in a database server of a service provider,and the DaaS adopts the encryption for data privacy and indexing for data query.However,an attacker can obtain ...In a database-as-a-service(DaaS)model,a data owner stores data in a database server of a service provider,and the DaaS adopts the encryption for data privacy and indexing for data query.However,an attacker can obtain original data’s statistical information and distribution via the indexing distribution from the database of the service provider.In this work,a novel indexing schema is proposed to satisfy privacy-preserved data management requirements,in which an attacker cannot obtain data source distribution or statistic information from the index.The approach includes 2 parts:the Hash-based indexing for encrypted data and correctness verification for range queries.The evaluation results demonstrate that the approach can hide statistical information of encrypted data distribution while can also obtain correct answers for range queries.Meanwhile,the approach can achieve nearly 10 times and 35 times improvement on encrypted data publishing and indexing respectively,compared with the start-of-the-art method order-preserving Hash-based function(OPHF).展开更多
Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malwar...Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.展开更多
With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices ge...With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.展开更多
The landscape of cybersecurity is rapidly evolving due to the advancement and integration of Artificial Intelligence (AI) and Machine Learning (ML). This paper explores the crucial role of AI and ML in enhancing cyber...The landscape of cybersecurity is rapidly evolving due to the advancement and integration of Artificial Intelligence (AI) and Machine Learning (ML). This paper explores the crucial role of AI and ML in enhancing cybersecurity defenses against increasingly sophisticated cyber threats, while also highlighting the new vulnerabilities introduced by these technologies. Through a comprehensive analysis that includes historical trends, technological evaluations, and predictive modeling, the dual-edged nature of AI and ML in cybersecurity is examined. Significant challenges such as data privacy, continuous training of AI models, manipulation risks, and ethical concerns are addressed. The paper emphasizes a balanced approach that leverages technological innovation alongside rigorous ethical standards and robust cybersecurity practices. This approach facilitates collaboration among various stakeholders to develop guidelines that ensure responsible and effective use of AI in cybersecurity, aiming to enhance system integrity and privacy without compromising security.展开更多
Intelligent vehicle applications provide convenience but raise privacy and security concerns.Misuse of sensitive data,including vehicle location,and facial recognition information,poses a threat to user privacy.Hence,...Intelligent vehicle applications provide convenience but raise privacy and security concerns.Misuse of sensitive data,including vehicle location,and facial recognition information,poses a threat to user privacy.Hence,traffic classification is vital for promptly overseeing and controlling applications with sensitive information.In this paper,we propose ETNet,a framework that combines multiple features and leverages self-attention mechanisms to learn deep relationships between packets.ET-Net employs a multisimilarity triplet network to extract features from raw bytes,and exploits self-attention to capture long-range dependencies within packets in a session and contextual information features.Additionally,we utilizing the loss function to more effectively integrate information acquired from both byte sequences and their corresponding lengths.Through simulated evaluations on datasets with similar attributes,ET-Net demonstrates the ability to finely distinguish between nine categories of applications,achieving superior results compared to existing methods.展开更多
Memory-unsafe programming languages,such as C/C++,are often used to develop system programs,rendering the programs susceptible to a variety of memory corruption attacks.Among these threats,just-in-time return-oriented...Memory-unsafe programming languages,such as C/C++,are often used to develop system programs,rendering the programs susceptible to a variety of memory corruption attacks.Among these threats,just-in-time return-oriented programming(JIT-ROP)stands out as an advanced method for conducting code-reuse attacks,effectively circumventing code randomization safeguards.JIT-ROP leverages memory disclosure vulnerabilities to obtain reusable code fragments dynamically and assemble malicious payloads dynamically.In response to JIT-ROP attacks,several re-randomization implementations have been developed to prevent the use of disclosed code.However,existing re-randomization methods require recurrent re-randomization during program runtime according to fixed time windows or specific events such as system calls,incurring significant runtime overhead.In this paper,we present the design and implementation of PtrProxy,an efficient re-randomization approach on the AArch64 platform.Unlike previous methods that necessitate frequent runtime rerandomization or reply on unreliable triggering conditions,this approach triggers the re-randomization process by detecting the code page harvest operation,which is a fundamental operation of the JIT-ROP at-tacks,making our method more efficient and reliable than previous approaches.We evaluate PtrProxy on benchmarks and real-world applications.The evaluation results show that our approach can effectively protect programs from JIT-ROP attacks while introducing marginal runtime overhead.展开更多
In this paper,the application of agricultural big data in agricultural economic management is deeply explored,and its potential in promoting profit growth and innovation is analyzed.However,challenges persist in data ...In this paper,the application of agricultural big data in agricultural economic management is deeply explored,and its potential in promoting profit growth and innovation is analyzed.However,challenges persist in data collection and integration,limitations of analytical technologies,talent development,team building,and policy support when applying agricultural big data.Effective application strategies are proposed,including data-driven precision agriculture practices,construction of data integration and management platforms,data security and privacy protection strategies,as well as long-term planning and development strategies for agricultural big data,to maximize its impact on agricultural economic management.Future advancements require collaborative efforts in technological innovation,talent cultivation,and policy support,to realize the extensive application of agricultural big data in agricultural economic management and ensure sustainable industrial development.展开更多
基金Supported by the National Key Technology Research and Development Program in the 12th Five year Plan of China(2012BAH08B02)the National Natural Science Foundation of China(61272513)the Project of Humanities and Social Sciences of Ministry of Education in China(10YJC630385)
文摘Aiming at the issues of privacy security in Internet of Things （IoT） applications, we propose an effective risk assessment model to handle probabilistic causality of evaluation factors and derive weights of influence-relation of propagation paths. The model undertakes probabilistic inference and generates values of risk probability for assets and propagation paths by using Bayesian causal relation-network and prior probability. According to Bayes- ian network （BN） structure, the risk analysts can easily find out relevant risk propagation paths and calculate weight values of each path by using decision-making trial and evaluation laboratory （DEMATEL）. This model is applied to determine the risk level of assets and each risk propagation path as well as implement countermeasure of recommendation in accordance with evaluation results. The simulation analysis shows that this model efficiently revises recommendation of countermeasures for decision-makers and mitigates risk to an acceptable range, in addition, it provides the theoretical basis for decision-making of privacy security risk assessment （PSRA） for further development in lot area.
文摘Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data.However,there is still a potential risk of privacy leakage,for example,attackers can obtain the original data through model inference attacks.Therefore,safeguarding the privacy of model parameters becomes crucial.One proposed solution involves incorporating homomorphic encryption algorithms into the federated learning process.However,the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal nodes.To solve the above problems,this paper proposes a privacy protection scheme named Federated Learning-Elastic Averaging Stochastic Gradient Descent(FL-EASGD)based on a fully homomorphic encryption algorithm.First,this paper introduces the homomorphic encryption algorithm into the FL-EASGD scheme to preventmodel plaintext leakage and realize privacy security in the process ofmodel aggregation.Second,this paper designs a robust model aggregation algorithm by adding time variables and constraint coefficients,which ensures the accuracy of model prediction while solving performance differences such as computation speed and node anomalies such as downtime of each participant.In addition,the scheme in this paper preserves the independent exploration of the local model by the nodes of each party,making the model more applicable to the local data distribution.Finally,experimental analysis shows that when there are abnormalities in the participants,the efficiency and accuracy of the whole protocol are not significantly affected.
基金supported in part by the National Natural Science Foundation of China under Grants(62250410365,62071084)the Youth Program of Humanities and Social Sciences of the MoE(23YJCZH291)+1 种基金the Key Laboratory of Computing Power Network and Information Security,Ministry of Education(2023ZD02)Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/15/46.
文摘As the 5G architecture gains momentum,interest in 6G is growing.The proliferation of Internet of Things(IoT)devices,capable of capturing sensitive images,has increased the need for secure transmission and robust access control mechanisms.The vast amount of data generated by low-computing devices poses a challenge to traditional centralized access control,which relies on trusted third parties and complex computations,resulting in intricate interactions,higher hardware costs,and processing delays.To address these issues,this paper introduces a novel distributed access control approach that integrates a decentralized and lightweight encryption mechanism with image transmission.This method enhances data security and resource efficiency without imposing heavy computational and network burdens.In comparison to the best existing approach,it achieves a 7%improvement in accuracy,effectively addressing existing gaps in lightweight encryption and recognition performance.
基金supported by the 2021 National Social Sciencetitled“Research on the Legal Risks and Prevention of China’s Social Media Platforms Operating in the United States”(21BXW040)
文摘The Metaverse is the digitization of the real world,supported by big data,AI,5G,cloud computing,blockchain,encryption algorithm,perception technology,digital twin,virtual engine,and other technologies that interact with human behavior and thoughts in avatars through digital identity.Cracking the trust problem brought by the avatar depends on the privacy security and authentication technology for individuals using digital identities to enter the Metaverse.To accomplish personal domination of the avatar,metaverse users need privacy data feeding and emotion projection.They must be equipped with proprietary algorithms to process and analyze the complex data generated in adaptive interactions,which challenges the privacy security of user data in the Metaverse.Distinguishing the significance of different identifiers in personal identity generation while imposing different behavioral regulatory requirements on data processing levels may better balance the relationship between personal privacy security and digital identity protection and data utilization in the Metaverse.In response to digital identity issues,there is an objective need to establish a unified digital identity authentication system to gain the general trust of society.Further,the remedies for a right to personality can be applied to the scenario of unlawful infringement of digital identity and privacy security.
文摘While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. In this paper, we firstly reviewed the enormous benefits and challenges of security and privacy in Big Data. Then, we present some possible methods and techniques to ensure Big Data security and privacy.
基金supported by project TRANSACT funded under H2020-EU.2.1.1.-INDUSTRIAL LEADERSHIP-Leadership in Enabling and Industrial Technologies-Information and Communication Technologies(Grant Agreement ID:101007260).
文摘The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different fields.Physical settings are probed for data,which is then transferred via linked networks.There are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny sensors.When it comes to deploying IoT,everyone agrees that security is the biggest issue.This is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square footage.This research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted circumstances.Using lightweight cryptography,the information sent between these gadgets may be secured.In order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance features.We also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.
基金supported in part by the National Natural Science Foundation of China (62072248, 62072247)the Jiangsu Agriculture Science and Technology Innovation Fund (CX(21)3060)。
文摘Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.
文摘The study of vehicular ad-hoc networks(VANETs)has received significant attention among academia;even so,its security and privacy still become a central issue that is wide-open to discuss.The authentication schemes deployed in VANETs have a substantial impact on its security and privacy.Many researchers have proposed a variety of schemes related to the information verification and efficiency improvement in VANETs.In recent years,many papers have proposed identity-based batch verification(IBV)schemes in regard to diminishing overhead in the message verification process in VANETs.This survey begins with providing background information about VANETs and clarifying its security and privacy,as well as performance requirements that must be satisfied.After presenting an outlook of some relevant surveys of VANETs,a brief review of some IBV schemes published in recent years is conferred.The detailed approach of each scheme,with a comprehensive comparison between them,has been provided afterward.Finally,we summarize those recent studies and possible future improvements.
文摘Modern communication allows billions of objects in the physical world as well as virtual environments to exchange data with each other in an autonomous way so as to create smart environments. However, modern communication also introduces new challenges for the security of systems and processes and the privacy of individuals. There is an increasing demand for development of new security and privacy approaches to guarantee the security, privacy, integ- rity, and availability of resources in modern communication.
文摘The Personal Information Protection Law,as the first law on personal information protection in China,hits the people’s most concerned,realistic and direct privacy and information security issues,and plays an extremely important role in promoting the development of the digital economy,the legalization of socialism with Chinese characteristics and social public security,and marks a new historical development stage in the protection of personal information in China.However,the awareness of privacy protection and privacy protection behavior of the public in personal information privacy protection is weak.Based on the literature review and in-depth understanding of current legal regulations,this study integrates the relevant literature and theoretical knowledge of the Personal Protection Law to construct a conceptual model of“privacy information protection willingness-privacy information protection behavior”.Taking the residents of Foshan City as an example,this paper conducts a questionnaire survey on their attitudes toward the Personal Protection Law,analyzes the factors influencing their willingness to protect their privacy and their behaviors,and explores the mechanisms of their influencing variables,to provide advice and suggestions for promoting the protection of privacy information and building a security barrier for the high-quality development of public information security.
基金The Universiti Kebangsaan Malaysia(UKM)Research Grant Scheme GGPM-2020-028 funded this research.
文摘The fifth generation(5G)system is the forthcoming generation of the mobile communication system.It has numerous additional features and offers an extensively high data rate,more capacity,and low latency.However,these features and applications have many problems and issues in terms of security,which has become a great challenge in the telecommunication industry.This paper aimed to propose a solution to preserve the user identity privacy in the 5G system that can identify permanent identity by using Variable Mobile Subscriber Identity,which randomly changes and does not use the permanent identity between the user equipment and home network.Through this mechanism,the user identity privacy would be secured and hidden.Moreover,it improves the synchronization between mobile users and home networks.Additionally,its compliance with the Authentication and Key Agreement(AKA)structure was adopted in the previous generations.It can be deployed efficiently in the preceding generations because the current architecture imposes minimal modifications on the network parties without changes in the authentication vector’s message size.Moreover,the addition of any hardware to the AKA carries minor adjustments on the network parties.In this paper,the ProVerif is used to verify the proposed scheme.
基金supported by National Key Technology Support Program(No.2013BAD17B06)Major Program of National Social Science Fund(No.15ZDB154)
文摘Big data has been taken as a Chinese national strategy in order to satisfy the developments of the social and economic requirements and the development of new information technology. The prosperity of big data brings not only convenience to people's daily life and more opportunities to enterprises, but more challenges with information security as well. This paper has a research on new types and features of information security issues in the age of big data, and puts forward the solutions for the above issues: build up the big data security management platform, set up the establishment of information security system and implement relevant laws and regulations.
文摘The ever-increasing needs of Internet of Things networks (IoTn) present considerable issues in computing complexity, security, trust, and authentication, among others. This gets increasingly more challenging as technology advances, and its use expands. As a consequence, boosting the capacity of these networks has garnered widespread attention. As a result, 5G, the next phase of cellular networks, is expected to be a game-changer, bringing with it faster data transmission rates, more capacity, improved service quality, and reduced latency. However, 5G networks continue to confront difficulties in establishing pervasive and dependable connections amongst high-speed IoT devices. Thus, to address the shortcomings in current recommendations, we present a unified architecture based on software-defined networks (SDNs) that provides 5G-enabled devices that must have complete secrecy. Through SDN, the architecture streamlines network administration while optimizing network communications. A mutual authentication protocol using elliptic curve cryptography is introduced for mutual authentication across certificate authorities and clustered heads in IoT network deployments based on IoT. Again, a dimensionality reduction intrusion detection mechanism is introduced to decrease computational cost and identify possible network breaches. However, to leverage the method’s potential, the initial module's security is reviewed. The second module is evaluated and compared to modern models.
基金the National Natural Science Foundation of China(No.61931019).
文摘In a database-as-a-service(DaaS)model,a data owner stores data in a database server of a service provider,and the DaaS adopts the encryption for data privacy and indexing for data query.However,an attacker can obtain original data’s statistical information and distribution via the indexing distribution from the database of the service provider.In this work,a novel indexing schema is proposed to satisfy privacy-preserved data management requirements,in which an attacker cannot obtain data source distribution or statistic information from the index.The approach includes 2 parts:the Hash-based indexing for encrypted data and correctness verification for range queries.The evaluation results demonstrate that the approach can hide statistical information of encrypted data distribution while can also obtain correct answers for range queries.Meanwhile,the approach can achieve nearly 10 times and 35 times improvement on encrypted data publishing and indexing respectively,compared with the start-of-the-art method order-preserving Hash-based function(OPHF).
基金This researchwork is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R411),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.
基金supported by the Shandong Province Science and Technology Project(2023TSGC0509,2022TSGC2234)Qingdao Science and Technology Plan Project(23-1-5-yqpy-2-qy)Open Topic Grants of Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving,Anhui Jianzhu University(IBES2024KF08).
文摘With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.
文摘The landscape of cybersecurity is rapidly evolving due to the advancement and integration of Artificial Intelligence (AI) and Machine Learning (ML). This paper explores the crucial role of AI and ML in enhancing cybersecurity defenses against increasingly sophisticated cyber threats, while also highlighting the new vulnerabilities introduced by these technologies. Through a comprehensive analysis that includes historical trends, technological evaluations, and predictive modeling, the dual-edged nature of AI and ML in cybersecurity is examined. Significant challenges such as data privacy, continuous training of AI models, manipulation risks, and ethical concerns are addressed. The paper emphasizes a balanced approach that leverages technological innovation alongside rigorous ethical standards and robust cybersecurity practices. This approach facilitates collaboration among various stakeholders to develop guidelines that ensure responsible and effective use of AI in cybersecurity, aiming to enhance system integrity and privacy without compromising security.
基金supported by National Key Research and Development Program of China(2022YFB3104903)S&T Program of Hebei(No.SZX2020034).
文摘Intelligent vehicle applications provide convenience but raise privacy and security concerns.Misuse of sensitive data,including vehicle location,and facial recognition information,poses a threat to user privacy.Hence,traffic classification is vital for promptly overseeing and controlling applications with sensitive information.In this paper,we propose ETNet,a framework that combines multiple features and leverages self-attention mechanisms to learn deep relationships between packets.ET-Net employs a multisimilarity triplet network to extract features from raw bytes,and exploits self-attention to capture long-range dependencies within packets in a session and contextual information features.Additionally,we utilizing the loss function to more effectively integrate information acquired from both byte sequences and their corresponding lengths.Through simulated evaluations on datasets with similar attributes,ET-Net demonstrates the ability to finely distinguish between nine categories of applications,achieving superior results compared to existing methods.
基金supported in part by the National Natural Science Foundation of China(62272351,61972297,62172308).
文摘Memory-unsafe programming languages,such as C/C++,are often used to develop system programs,rendering the programs susceptible to a variety of memory corruption attacks.Among these threats,just-in-time return-oriented programming(JIT-ROP)stands out as an advanced method for conducting code-reuse attacks,effectively circumventing code randomization safeguards.JIT-ROP leverages memory disclosure vulnerabilities to obtain reusable code fragments dynamically and assemble malicious payloads dynamically.In response to JIT-ROP attacks,several re-randomization implementations have been developed to prevent the use of disclosed code.However,existing re-randomization methods require recurrent re-randomization during program runtime according to fixed time windows or specific events such as system calls,incurring significant runtime overhead.In this paper,we present the design and implementation of PtrProxy,an efficient re-randomization approach on the AArch64 platform.Unlike previous methods that necessitate frequent runtime rerandomization or reply on unreliable triggering conditions,this approach triggers the re-randomization process by detecting the code page harvest operation,which is a fundamental operation of the JIT-ROP at-tacks,making our method more efficient and reliable than previous approaches.We evaluate PtrProxy on benchmarks and real-world applications.The evaluation results show that our approach can effectively protect programs from JIT-ROP attacks while introducing marginal runtime overhead.
基金Supported by Research and Application of Soil Collection Software and Soil Ecological Big Data Platform in Guangxi Woodland(GUILINKEYAN[2022ZC]44)Construction of Soil Information Database and Visualization System for Artificial Forests in Central Guangxi(2023GXZCLK62).
文摘In this paper,the application of agricultural big data in agricultural economic management is deeply explored,and its potential in promoting profit growth and innovation is analyzed.However,challenges persist in data collection and integration,limitations of analytical technologies,talent development,team building,and policy support when applying agricultural big data.Effective application strategies are proposed,including data-driven precision agriculture practices,construction of data integration and management platforms,data security and privacy protection strategies,as well as long-term planning and development strategies for agricultural big data,to maximize its impact on agricultural economic management.Future advancements require collaborative efforts in technological innovation,talent cultivation,and policy support,to realize the extensive application of agricultural big data in agricultural economic management and ensure sustainable industrial development.
