Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008,blockchain has(slowly)become one of the most frequently discussed methods for securing data storage and transfer through decentralized,tru...Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008,blockchain has(slowly)become one of the most frequently discussed methods for securing data storage and transfer through decentralized,trustless,peer-to-peer systems.This research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security applications.Our findings show that the Internet of Things(IoT)lends itself well to novel blockchain applications,as do networks and machine visualization,public-key cryptography,web applications,certification schemes and the secure storage of Personally Identifiable Information(PII).This timely systematic review also sheds light on future directions of research,education and practices in the blockchain and cyber security space,such as security of blockchain in IoT,security of blockchain for AI data,and sidechain security.展开更多
In recent years,immense developments have occurred in the field of Artificial Intelligence(AI)and the spread of broadband and ubiquitous connectivity technologies.This has led to the development and commercialization ...In recent years,immense developments have occurred in the field of Artificial Intelligence(AI)and the spread of broadband and ubiquitous connectivity technologies.This has led to the development and commercialization of Digital Twin(DT)technology.The widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks(DTNs),which orchestrate through the networks of ubiquitous DTs and their corresponding physical assets.DTNs create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing,computing,and DT modeling.The high volume of user data and the ubiquitous communication systems in DTNs come with their own set of challenges.The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches.Also,currently,there is not enough literature that focuses on privacy and security issues in DTN applications.In this survey,we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their performance.Next,we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective components.We then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the DTNs.We also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the issues.Finally,we provide some open research issues and problems in the field of DTN privacy and security.展开更多
Federated Learning(FL)is currently a widely used collaborative learning framework,and the distinguished feature of FL is that the clients involved in training do not need to share raw data,but only transfer the model ...Federated Learning(FL)is currently a widely used collaborative learning framework,and the distinguished feature of FL is that the clients involved in training do not need to share raw data,but only transfer the model parameters to share knowledge,and finally get a global model with improved performance.However,recent studies have found that sharing model parameters may still lead to privacy leakage.From the shared model parameters,local training data can be reconstructed and thus lead to a threat to individual privacy and security.We observed that most of the current attacks are aimed at client-specific data reconstruction,while limited attention is paid to the information leakage of the global model.In our work,we propose a novel FL attack based on shared model parameters that can deduce the data distribution of the global model.Different from other FL attacks that aim to infer individual clients’raw data,the data distribution inference attack proposed in this work shows that the attackers can have the capability to deduce the data distribution information behind the global model.We argue that such information is valuable since the training data behind a welltrained global model indicates the common knowledge of a specific task,such as social networks and e-commerce applications.To implement such an attack,our key idea is to adopt a deep reinforcement learning approach to guide the attack process,where the RL agent adjusts the pseudo-data distribution automatically until it is similar to the ground truth data distribution.By a carefully designed Markov decision proces(MDP)process,our implementation ensures our attack can have stable performance and experimental results verify the effectiveness of our proposed inference attack.展开更多
Accurate and efficient bacterial detection is essential for public health and medical diagnostics. However, traditional detection methods are constrained by limited dataset size, complex bacterial morphology, and dive...Accurate and efficient bacterial detection is essential for public health and medical diagnostics. However, traditional detection methods are constrained by limited dataset size, complex bacterial morphology, and diverse detection environments, hindering their effectiveness. In this study, we present EagleEyeNet, a novel multi-scale information fusion model designed to address these challenges. EagleEyeNet leverages large models as teacher networks in a knowledge distillation framework, significantly improving detection performance. Additionally, a newly designed feature fusion architecture, integrating Transformer modules, is proposed to enable the efficient fusion of global and multi-scale features, overcoming the bottlenecks posed by Feature Pyramid Networks (FPN) structures, which in turn reduces information transmission loss between feature layers. To improve the model’s adaptability for different scenarios, we create our own QingDao Bacteria Detection (QDBD) dataset as a comprehensive evaluation benchmark for bacterial detection. Experimental results demonstrate that EagleEyeNet achieves remarkable performance improvements, with mAP50 increases of 3.1% on the QDBD dataset and 4.9% on the AGRA dataset, outperforming the State-Of-The-Art (SOTA) methods in detection accuracy. These findings underscore the transformative potential of integrating large models and deep learning for advancing bacterial detection technologies.展开更多
Metaverse describes a new shape of cyberspace and has become a hot-trending word since 2021.There are many explanations about what Meterverse is and attempts to provide a formal standard or definition of Metaverse.How...Metaverse describes a new shape of cyberspace and has become a hot-trending word since 2021.There are many explanations about what Meterverse is and attempts to provide a formal standard or definition of Metaverse.However,these definitions could hardly reach universal acceptance.Rather than providing a formal definition of the Metaverse,we list four must-have characteristics of the Metaverse:socialization,immersive interaction,real world-building,and expandability.These characteristics not only carve the Metaverse into a novel and fantastic digital world,but also make it suffer from all security/privacy risks,such as personal information leakage,eavesdropping,unauthorized access,phishing,data injection,broken authentication,insecure design,and more.This paper first introduces the four characteristics,then the current progress and typical applications of the Metaverse are surveyed and categorized into four economic sectors.Based on the four characteristics and the findings of the current progress,the security and privacy issues in the Metaverse are investigated.We then identify and discuss more potential critical security and privacy issues that can be caused by combining the four characteristics.Lastly,the paper also raises some other concerns regarding society and humanity.展开更多
文摘Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008,blockchain has(slowly)become one of the most frequently discussed methods for securing data storage and transfer through decentralized,trustless,peer-to-peer systems.This research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security applications.Our findings show that the Internet of Things(IoT)lends itself well to novel blockchain applications,as do networks and machine visualization,public-key cryptography,web applications,certification schemes and the secure storage of Personally Identifiable Information(PII).This timely systematic review also sheds light on future directions of research,education and practices in the blockchain and cyber security space,such as security of blockchain in IoT,security of blockchain for AI data,and sidechain security.
基金supported in part by the National Science Foundation(NSF)of the USA(2146497,2416872,2315596 and 2244219).
文摘In recent years,immense developments have occurred in the field of Artificial Intelligence(AI)and the spread of broadband and ubiquitous connectivity technologies.This has led to the development and commercialization of Digital Twin(DT)technology.The widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks(DTNs),which orchestrate through the networks of ubiquitous DTs and their corresponding physical assets.DTNs create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing,computing,and DT modeling.The high volume of user data and the ubiquitous communication systems in DTNs come with their own set of challenges.The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches.Also,currently,there is not enough literature that focuses on privacy and security issues in DTN applications.In this survey,we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their performance.Next,we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective components.We then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the DTNs.We also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the issues.Finally,we provide some open research issues and problems in the field of DTN privacy and security.
文摘Federated Learning(FL)is currently a widely used collaborative learning framework,and the distinguished feature of FL is that the clients involved in training do not need to share raw data,but only transfer the model parameters to share knowledge,and finally get a global model with improved performance.However,recent studies have found that sharing model parameters may still lead to privacy leakage.From the shared model parameters,local training data can be reconstructed and thus lead to a threat to individual privacy and security.We observed that most of the current attacks are aimed at client-specific data reconstruction,while limited attention is paid to the information leakage of the global model.In our work,we propose a novel FL attack based on shared model parameters that can deduce the data distribution of the global model.Different from other FL attacks that aim to infer individual clients’raw data,the data distribution inference attack proposed in this work shows that the attackers can have the capability to deduce the data distribution information behind the global model.We argue that such information is valuable since the training data behind a welltrained global model indicates the common knowledge of a specific task,such as social networks and e-commerce applications.To implement such an attack,our key idea is to adopt a deep reinforcement learning approach to guide the attack process,where the RL agent adjusts the pseudo-data distribution automatically until it is similar to the ground truth data distribution.By a carefully designed Markov decision proces(MDP)process,our implementation ensures our attack can have stable performance and experimental results verify the effectiveness of our proposed inference attack.
基金supported by the Shandong Provincial Department of Education.
文摘Accurate and efficient bacterial detection is essential for public health and medical diagnostics. However, traditional detection methods are constrained by limited dataset size, complex bacterial morphology, and diverse detection environments, hindering their effectiveness. In this study, we present EagleEyeNet, a novel multi-scale information fusion model designed to address these challenges. EagleEyeNet leverages large models as teacher networks in a knowledge distillation framework, significantly improving detection performance. Additionally, a newly designed feature fusion architecture, integrating Transformer modules, is proposed to enable the efficient fusion of global and multi-scale features, overcoming the bottlenecks posed by Feature Pyramid Networks (FPN) structures, which in turn reduces information transmission loss between feature layers. To improve the model’s adaptability for different scenarios, we create our own QingDao Bacteria Detection (QDBD) dataset as a comprehensive evaluation benchmark for bacterial detection. Experimental results demonstrate that EagleEyeNet achieves remarkable performance improvements, with mAP50 increases of 3.1% on the QDBD dataset and 4.9% on the AGRA dataset, outperforming the State-Of-The-Art (SOTA) methods in detection accuracy. These findings underscore the transformative potential of integrating large models and deep learning for advancing bacterial detection technologies.
基金supported by the National Natural Science Foundation of China(No.1912753).
文摘Metaverse describes a new shape of cyberspace and has become a hot-trending word since 2021.There are many explanations about what Meterverse is and attempts to provide a formal standard or definition of Metaverse.However,these definitions could hardly reach universal acceptance.Rather than providing a formal definition of the Metaverse,we list four must-have characteristics of the Metaverse:socialization,immersive interaction,real world-building,and expandability.These characteristics not only carve the Metaverse into a novel and fantastic digital world,but also make it suffer from all security/privacy risks,such as personal information leakage,eavesdropping,unauthorized access,phishing,data injection,broken authentication,insecure design,and more.This paper first introduces the four characteristics,then the current progress and typical applications of the Metaverse are surveyed and categorized into four economic sectors.Based on the four characteristics and the findings of the current progress,the security and privacy issues in the Metaverse are investigated.We then identify and discuss more potential critical security and privacy issues that can be caused by combining the four characteristics.Lastly,the paper also raises some other concerns regarding society and humanity.
