Zero-trust security is a novel concept to cope with intricate access,which can not be handled by the conventional perimeter-based architecture anymore.The device-to-device continuous authentication protocol is one of ...Zero-trust security is a novel concept to cope with intricate access,which can not be handled by the conventional perimeter-based architecture anymore.The device-to-device continuous authentication protocol is one of the most crucial cornerstones,especially in the IoT scenario.In the zero-trust architecture,trust does not rely on any position,person or device.However,to the best of our knowledge,almost all existing device-to-device continuous authentication relies on a trust authority or a node to generate secret keys or secret values.This is betrayed by the principle of zero-trust architecture.In this paper,we employ the blockchain to eliminate the trusted node.One node is chosen to produce the public parameter and secret keys for two entities through the practical Byzantine fault tolerance consensus mechanism.Additionally,the devices are categorized into three folds:trusted device,suspected device and untrusted device.Only the first two can participate in authentication,and they have different lengths of security parameters and intervals to reach a better balance between security and efficiency.Then we prove the security of the initial authentication part in the eCK model and give an informal analysis of the continuous authentication part.Finally,we implement the proposed protocol on simulated devices.The result illustrates that our scheme is highly efficient,and the continuous authentication only costs around 0.1ms.展开更多
The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like,fingerprint or face.Implicit continuou...The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like,fingerprint or face.Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users’identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session.However,divergent issues remain unaddressed.This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called,Gaussian Weighted Cauchy Kriging-based Continuous Czekanowski’s(GWCK-CC).First,a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise pre-sent in the raw input face images.Cauchy Kriging Regression function is employed to reduce the dimensionality.Finally,Continuous Czekanowski’s Clas-sification is utilized for proficient classification between the genuine user and attacker.By this way,the proposed GWCK-CC method achieves accurate authen-tication with minimum error rate and time.Experimental assessment of the pro-posed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset.The results confirm that the proposed GWCK-CC method enhances authentication accuracy,by 9%,reduces the authen-tication time,and error rate by 44%,and 43%as compared to the existing methods.展开更多
Keystroke-based behavioral biometrics have been proven effective for continuous user authentication.Current state-of-the-art algorithms have achieved outstanding results in long text or short text collected by doing s...Keystroke-based behavioral biometrics have been proven effective for continuous user authentication.Current state-of-the-art algorithms have achieved outstanding results in long text or short text collected by doing some tasks.It remains a considerable challenge to authenticate users continuously and accurately with short keystroke inputs collected in uncontrolled settings.In this work,we propose a Timely Keystroke-based method for Continuous user Authentication,named TKCA.It integrates the key name and two kinds of timing features through an embedding mechanism.And it captures the relationship between context keystrokes by the Bidirectional Long Short-Term Memory(Bi-LSTM)network.We conduct a series of experiments to validate it on a public dataset-the Clarkson II dataset collected in a completely uncontrolled and natural setting.Experiment results show that the proposed TKCA achieves state-of-the-art performance with 8.28%of EER when using only 30 keystrokes and 2.78%of EER when using 190 keystrokes.展开更多
基金supported in part by the National Science Foundation Project of China(No.61931001)the Scientific and Technological Innovation Foundation of Foshan,USTB(No.BK20AF003).
文摘Zero-trust security is a novel concept to cope with intricate access,which can not be handled by the conventional perimeter-based architecture anymore.The device-to-device continuous authentication protocol is one of the most crucial cornerstones,especially in the IoT scenario.In the zero-trust architecture,trust does not rely on any position,person or device.However,to the best of our knowledge,almost all existing device-to-device continuous authentication relies on a trust authority or a node to generate secret keys or secret values.This is betrayed by the principle of zero-trust architecture.In this paper,we employ the blockchain to eliminate the trusted node.One node is chosen to produce the public parameter and secret keys for two entities through the practical Byzantine fault tolerance consensus mechanism.Additionally,the devices are categorized into three folds:trusted device,suspected device and untrusted device.Only the first two can participate in authentication,and they have different lengths of security parameters and intervals to reach a better balance between security and efficiency.Then we prove the security of the initial authentication part in the eCK model and give an informal analysis of the continuous authentication part.Finally,we implement the proposed protocol on simulated devices.The result illustrates that our scheme is highly efficient,and the continuous authentication only costs around 0.1ms.
文摘The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like,fingerprint or face.Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users’identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session.However,divergent issues remain unaddressed.This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called,Gaussian Weighted Cauchy Kriging-based Continuous Czekanowski’s(GWCK-CC).First,a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise pre-sent in the raw input face images.Cauchy Kriging Regression function is employed to reduce the dimensionality.Finally,Continuous Czekanowski’s Clas-sification is utilized for proficient classification between the genuine user and attacker.By this way,the proposed GWCK-CC method achieves accurate authen-tication with minimum error rate and time.Experimental assessment of the pro-posed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset.The results confirm that the proposed GWCK-CC method enhances authentication accuracy,by 9%,reduces the authen-tication time,and error rate by 44%,and 43%as compared to the existing methods.
基金the National Key R&D Program of China(Grant No.2016YFB0801002).
文摘Keystroke-based behavioral biometrics have been proven effective for continuous user authentication.Current state-of-the-art algorithms have achieved outstanding results in long text or short text collected by doing some tasks.It remains a considerable challenge to authenticate users continuously and accurately with short keystroke inputs collected in uncontrolled settings.In this work,we propose a Timely Keystroke-based method for Continuous user Authentication,named TKCA.It integrates the key name and two kinds of timing features through an embedding mechanism.And it captures the relationship between context keystrokes by the Bidirectional Long Short-Term Memory(Bi-LSTM)network.We conduct a series of experiments to validate it on a public dataset-the Clarkson II dataset collected in a completely uncontrolled and natural setting.Experiment results show that the proposed TKCA achieves state-of-the-art performance with 8.28%of EER when using only 30 keystrokes and 2.78%of EER when using 190 keystrokes.
