Radio frequency fingerprint identification(RFFI)shows great potential as a means for authenticating wireless devices.As RFFI can be addressed as a classification problem,deep learning techniques are widely utilized in...Radio frequency fingerprint identification(RFFI)shows great potential as a means for authenticating wireless devices.As RFFI can be addressed as a classification problem,deep learning techniques are widely utilized in modern RFFI systems for their outstanding performance.RFFI is suitable for securing the legacy existing Internet of Things(IoT)networks since it does not require any modifications to the existing end-node hardware and communication protocols.However,most deep learning-based RFFI systems require the collection of a great number of labelled signals for training,which is time-consuming and not ideal,especially for the Io T end nodes that are already deployed and configured with long transmission intervals.Moreover,the long time required to train a neural network from scratch also limits rapid deployment on legacy Io T networks.To address the above issues,two transferable RFFI protocols are proposed in this paper leveraging the concept of transfer learning.More specifically,they rely on fine-tuning and distance metric learning,respectively,and only require only a small amount of signals from the legacy IoT network.As the dataset used for transfer is small,we propose to apply augmentation in the transfer process to generate more training signals to improve performance.A Lo Ra-RFFI testbed consisting of 40 commercial-off-the-shelf(COTS)Lo Ra IoT devices and a software-defined radio(SDR)receiver is built to experimentally evaluate the proposed approaches.The experimental results demonstrate that both the fine-tuning and distance metric learning-based RFFI approaches can be rapidly transferred to another Io T network with less than ten signals from each Lo Ra device.The classification accuracy is over 90%,and the augmentation technique can improve the accuracy by up to 20%.展开更多
The popularity of the Internet of Things(IoT)has enabled a large number of vulnerable devices to connect to the Internet,bringing huge security risks.As a network-level security authentication method,device fingerprin...The popularity of the Internet of Things(IoT)has enabled a large number of vulnerable devices to connect to the Internet,bringing huge security risks.As a network-level security authentication method,device fingerprint based on machine learning has attracted considerable attention because it can detect vulnerable devices in complex and heterogeneous access phases.However,flexible and diversified IoT devices with limited resources increase dif-ficulty of the device fingerprint authentication method executed in IoT,because it needs to retrain the model network to deal with incremental features or types.To address this problem,a device fingerprinting mechanism based on a Broad Learning System(BLS)is proposed in this paper.The mechanism firstly characterizes IoT devices by traffic analysis based on the identifiable differences of the traffic data of IoT devices,and extracts feature parameters of the traffic packets.A hierarchical hybrid sampling method is designed at the preprocessing phase to improve the imbalanced data distribution and reconstruct the fingerprint dataset.The complexity of the dataset is reduced using Principal Component Analysis(PCA)and the device type is identified by training weights using BLS.The experimental results show that the proposed method can achieve state-of-the-art accuracy and spend less training time than other existing methods.展开更多
Smart home devices are vulnerable to a variety of attacks.The matter gets more complicated when a number of devices collaborate to launch a colluding attack(e.g.,Distributed-Denial-of-Service(DDoS))in a network(e.g.,S...Smart home devices are vulnerable to a variety of attacks.The matter gets more complicated when a number of devices collaborate to launch a colluding attack(e.g.,Distributed-Denial-of-Service(DDoS))in a network(e.g.,Smart home).To handle these attacks,most studies have hitherto proposed authentication protocols that cannot necessarily be implemented in devices,especially during Device-to-Device(D2D)interactions.Tapping into the potential of Ethereum blockchain and smart contracts,this work proposes a lightweight authentication mechanism that enables safe D2D interactions in a smart home.The Ethereum blockchain enables the implementation of a decentralized prototype as well as a peer-to-peer distributed ledger system.The work also uses a single server queuing system model and the authentication mechanism to curtail DDoS attacks by controlling the number of service requests in the system.The simulation was conducted twenty times,each with varying number of devices chosen at random(ranging from 1 to 30).Each requester device sends an arbitrary request with a unique resource requirement at a time.This is done to measure the system's consistency across a variety of device capabilities.The experimental results show that the proposed protocol not only prevents colluding attacks,but also outperforms the benchmark protocols in terms of computational cost,message processing,and response times.展开更多
Smart home devices are vulnerable to a variety of attacks.The matter gets more complicated when a number of devices collaborate to launch a colluding attack(e.g.,Distributed-Denial-of-Service(DDoS))in a network(e.g.,S...Smart home devices are vulnerable to a variety of attacks.The matter gets more complicated when a number of devices collaborate to launch a colluding attack(e.g.,Distributed-Denial-of-Service(DDoS))in a network(e.g.,Smart home).To handle these attacks,most studies have hitherto proposed authentication protocols that cannot necessarily be implemented in devices,especially during Device-to-Device(D2D)interactions.Tapping into the potential of Ethereum blockchain and smart contracts,this work proposes a lightweight authentication mechanism that enables safe D2D interactions in a smart home.The Ethereum blockchain enables the implementation of a decentralized prototype as well as a peer-to-peer distributed ledger system.The work also uses a single server queuing system model and the authentication mechanism to curtail DDoS attacks by controlling the number of service requests in the system.The simulation was conducted twenty times,each with varying number of devices chosen at random(ranging from 1 to 30).Each requester device sends an arbitrary request with a unique resource requirement at a time.This is done to measure the system's consistency across a variety of device capabilities.The experimental results show that the proposed protocol not only prevents colluding attacks,but also outperforms the benchmark protocols in terms of computational cost,message processing,and response times.展开更多
The mobile cellular network provides internet connectivity for heterogeneous Internet of Things(IoT)devices.The cellular network consists of several towers installed at appropriate locations within a smart city.These ...The mobile cellular network provides internet connectivity for heterogeneous Internet of Things(IoT)devices.The cellular network consists of several towers installed at appropriate locations within a smart city.These cellular towers can be utilized for various tasks,such as e-healthcare systems,smart city surveillance,traffic monitoring,infrastructure surveillance,or sidewalk checking.Security is a primary concern in data broadcasting,particularly authentication,because the strength of a cellular network’s signal is much higher frequency than the associated one,and their frequencies can sometimes be aligned,posing a significant challenge.As a result,that requires attention,and without information authentication,such a barrier cannot be removed.So,we design a secure and efficient information authentication scheme for IoT-enabled devices tomitigate the flaws in the e-healthcare system.The proposed protocol security shall check formally using the Real-or-Random(ROR)model,simulated using ProVerif2.03,and informally using pragmatic discussion.In comparison,the performance phenomenon shall tackle by the already result available in the MIRACL cryptographic lab.展开更多
基金in part supported by UK Engineering and Physical Sciences Research Council under grant ID EP/V027697/1in part by the National Key Research and Development Program of China under grant ID 2020YFE0200600
文摘Radio frequency fingerprint identification(RFFI)shows great potential as a means for authenticating wireless devices.As RFFI can be addressed as a classification problem,deep learning techniques are widely utilized in modern RFFI systems for their outstanding performance.RFFI is suitable for securing the legacy existing Internet of Things(IoT)networks since it does not require any modifications to the existing end-node hardware and communication protocols.However,most deep learning-based RFFI systems require the collection of a great number of labelled signals for training,which is time-consuming and not ideal,especially for the Io T end nodes that are already deployed and configured with long transmission intervals.Moreover,the long time required to train a neural network from scratch also limits rapid deployment on legacy Io T networks.To address the above issues,two transferable RFFI protocols are proposed in this paper leveraging the concept of transfer learning.More specifically,they rely on fine-tuning and distance metric learning,respectively,and only require only a small amount of signals from the legacy IoT network.As the dataset used for transfer is small,we propose to apply augmentation in the transfer process to generate more training signals to improve performance.A Lo Ra-RFFI testbed consisting of 40 commercial-off-the-shelf(COTS)Lo Ra IoT devices and a software-defined radio(SDR)receiver is built to experimentally evaluate the proposed approaches.The experimental results demonstrate that both the fine-tuning and distance metric learning-based RFFI approaches can be rapidly transferred to another Io T network with less than ten signals from each Lo Ra device.The classification accuracy is over 90%,and the augmentation technique can improve the accuracy by up to 20%.
基金supported by National Key R&D Program of China(2019YFB2102303)National Natural Science Foundation of China(NSFC61971014,NSFC11675199)Young Backbone Teacher Training Program of Henan Colleges and Universities(2021GGJS170).
文摘The popularity of the Internet of Things(IoT)has enabled a large number of vulnerable devices to connect to the Internet,bringing huge security risks.As a network-level security authentication method,device fingerprint based on machine learning has attracted considerable attention because it can detect vulnerable devices in complex and heterogeneous access phases.However,flexible and diversified IoT devices with limited resources increase dif-ficulty of the device fingerprint authentication method executed in IoT,because it needs to retrain the model network to deal with incremental features or types.To address this problem,a device fingerprinting mechanism based on a Broad Learning System(BLS)is proposed in this paper.The mechanism firstly characterizes IoT devices by traffic analysis based on the identifiable differences of the traffic data of IoT devices,and extracts feature parameters of the traffic packets.A hierarchical hybrid sampling method is designed at the preprocessing phase to improve the imbalanced data distribution and reconstruct the fingerprint dataset.The complexity of the dataset is reduced using Principal Component Analysis(PCA)and the device type is identified by training weights using BLS.The experimental results show that the proposed method can achieve state-of-the-art accuracy and spend less training time than other existing methods.
文摘Smart home devices are vulnerable to a variety of attacks.The matter gets more complicated when a number of devices collaborate to launch a colluding attack(e.g.,Distributed-Denial-of-Service(DDoS))in a network(e.g.,Smart home).To handle these attacks,most studies have hitherto proposed authentication protocols that cannot necessarily be implemented in devices,especially during Device-to-Device(D2D)interactions.Tapping into the potential of Ethereum blockchain and smart contracts,this work proposes a lightweight authentication mechanism that enables safe D2D interactions in a smart home.The Ethereum blockchain enables the implementation of a decentralized prototype as well as a peer-to-peer distributed ledger system.The work also uses a single server queuing system model and the authentication mechanism to curtail DDoS attacks by controlling the number of service requests in the system.The simulation was conducted twenty times,each with varying number of devices chosen at random(ranging from 1 to 30).Each requester device sends an arbitrary request with a unique resource requirement at a time.This is done to measure the system's consistency across a variety of device capabilities.The experimental results show that the proposed protocol not only prevents colluding attacks,but also outperforms the benchmark protocols in terms of computational cost,message processing,and response times.
文摘Smart home devices are vulnerable to a variety of attacks.The matter gets more complicated when a number of devices collaborate to launch a colluding attack(e.g.,Distributed-Denial-of-Service(DDoS))in a network(e.g.,Smart home).To handle these attacks,most studies have hitherto proposed authentication protocols that cannot necessarily be implemented in devices,especially during Device-to-Device(D2D)interactions.Tapping into the potential of Ethereum blockchain and smart contracts,this work proposes a lightweight authentication mechanism that enables safe D2D interactions in a smart home.The Ethereum blockchain enables the implementation of a decentralized prototype as well as a peer-to-peer distributed ledger system.The work also uses a single server queuing system model and the authentication mechanism to curtail DDoS attacks by controlling the number of service requests in the system.The simulation was conducted twenty times,each with varying number of devices chosen at random(ranging from 1 to 30).Each requester device sends an arbitrary request with a unique resource requirement at a time.This is done to measure the system's consistency across a variety of device capabilities.The experimental results show that the proposed protocol not only prevents colluding attacks,but also outperforms the benchmark protocols in terms of computational cost,message processing,and response times.
基金supported by the Natural Science Foundation of Beijing Municipality under Grant M21039.
文摘The mobile cellular network provides internet connectivity for heterogeneous Internet of Things(IoT)devices.The cellular network consists of several towers installed at appropriate locations within a smart city.These cellular towers can be utilized for various tasks,such as e-healthcare systems,smart city surveillance,traffic monitoring,infrastructure surveillance,or sidewalk checking.Security is a primary concern in data broadcasting,particularly authentication,because the strength of a cellular network’s signal is much higher frequency than the associated one,and their frequencies can sometimes be aligned,posing a significant challenge.As a result,that requires attention,and without information authentication,such a barrier cannot be removed.So,we design a secure and efficient information authentication scheme for IoT-enabled devices tomitigate the flaws in the e-healthcare system.The proposed protocol security shall check formally using the Real-or-Random(ROR)model,simulated using ProVerif2.03,and informally using pragmatic discussion.In comparison,the performance phenomenon shall tackle by the already result available in the MIRACL cryptographic lab.
