射频识别(RFID)技术已在社会各领域得到了广泛应用,如门禁系统、银行卡、居民身份证等。与此同时,不断出现的RFID克隆卡时刻威胁着RFID应用系统的安全。尽管目前已提出了多种安全机制,如基于密码学的认证协议,并假设"密钥不出卡&qu...射频识别(RFID)技术已在社会各领域得到了广泛应用,如门禁系统、银行卡、居民身份证等。与此同时,不断出现的RFID克隆卡时刻威胁着RFID应用系统的安全。尽管目前已提出了多种安全机制,如基于密码学的认证协议,并假设"密钥不出卡",但在侧信道分析等新型攻击手段下,这类安全机制被绕过的风险显著增加。此外,大量RFID卡的应用诸如门禁系统等并不使用密码技术,使RFID卡被克隆的风险更大。本文提出了一种基于物理层特性的射频指纹识别方法—"牵星"法,使高频RFID卡与其唯一且不可克隆的射频特征紧密绑定,从而有效检测高频RFID克隆卡。我们对来自同厂家、同型号、同批次的120张高频RFID卡进行了测试,识别精度可达等错误率EER=2.5%。本方法可直接用于所有基于ISO14443 Type A协议的高频RFID克隆卡检测。同时,由于该方法是对设备的射频指纹进行后期处理,因此也支持其它标准定义的RFID克隆卡的检测。该识别系统仅由一个天线、一个读卡器和一个示波器组成,是现有高频RFID卡识别系统中所需测量设备最少的一种。展开更多
With the vast advancements in Information Technology,the emergence of Online Social Networking(OSN)has also hit its peak and captured the atten-tion of the young generation people.The clone intends to replicate the us...With the vast advancements in Information Technology,the emergence of Online Social Networking(OSN)has also hit its peak and captured the atten-tion of the young generation people.The clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original user.However,the attackers also target this height of OSN utilization,explicitly creating the clones of the user’s account.Various clone detection mechanisms are designed based on social-network activities.For instance,monitoring the occur-rence of clone edges is done to restrict the generation of clone activities.However,this assumption is unsuitable for a real-time environment and works optimally during the simulation process.This research concentrates on modeling and effi-cient clone prediction and avoidance methods to help the social network activists and the victims enhance the clone prediction accuracy.This model does not rely on assumptions.Here,an ensemble Adaptive Random Subspace is used for clas-sifying the clone victims with k-Nearest Neighbour(k-NN)as a base classifier.The weighted clone nodes are analysed using the weighted graph theory concept based on the classified results.When the weighted node’s threshold value is high-er,the trust establishment is terminated,and the clones are ranked and sorted in the higher place for termination.Thus,the victims are alert to the clone propaga-tion over the online social networking end,and the validation is done using the MATLAB 2020a simulation environment.The model shows a better trade-off than existing approaches like Random Forest(RF),Naïve Bayes(NB),and the standard graph model.Various performance metrics like True Positive Rate(TPR),False Alarm Rate(FAR),Recall,Precision,F-measure,and ROC and run time analysis are evaluated to show the significance of the model.展开更多
文摘射频识别(RFID)技术已在社会各领域得到了广泛应用,如门禁系统、银行卡、居民身份证等。与此同时,不断出现的RFID克隆卡时刻威胁着RFID应用系统的安全。尽管目前已提出了多种安全机制,如基于密码学的认证协议,并假设"密钥不出卡",但在侧信道分析等新型攻击手段下,这类安全机制被绕过的风险显著增加。此外,大量RFID卡的应用诸如门禁系统等并不使用密码技术,使RFID卡被克隆的风险更大。本文提出了一种基于物理层特性的射频指纹识别方法—"牵星"法,使高频RFID卡与其唯一且不可克隆的射频特征紧密绑定,从而有效检测高频RFID克隆卡。我们对来自同厂家、同型号、同批次的120张高频RFID卡进行了测试,识别精度可达等错误率EER=2.5%。本方法可直接用于所有基于ISO14443 Type A协议的高频RFID克隆卡检测。同时,由于该方法是对设备的射频指纹进行后期处理,因此也支持其它标准定义的RFID克隆卡的检测。该识别系统仅由一个天线、一个读卡器和一个示波器组成,是现有高频RFID卡识别系统中所需测量设备最少的一种。
文摘With the vast advancements in Information Technology,the emergence of Online Social Networking(OSN)has also hit its peak and captured the atten-tion of the young generation people.The clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original user.However,the attackers also target this height of OSN utilization,explicitly creating the clones of the user’s account.Various clone detection mechanisms are designed based on social-network activities.For instance,monitoring the occur-rence of clone edges is done to restrict the generation of clone activities.However,this assumption is unsuitable for a real-time environment and works optimally during the simulation process.This research concentrates on modeling and effi-cient clone prediction and avoidance methods to help the social network activists and the victims enhance the clone prediction accuracy.This model does not rely on assumptions.Here,an ensemble Adaptive Random Subspace is used for clas-sifying the clone victims with k-Nearest Neighbour(k-NN)as a base classifier.The weighted clone nodes are analysed using the weighted graph theory concept based on the classified results.When the weighted node’s threshold value is high-er,the trust establishment is terminated,and the clones are ranked and sorted in the higher place for termination.Thus,the victims are alert to the clone propaga-tion over the online social networking end,and the validation is done using the MATLAB 2020a simulation environment.The model shows a better trade-off than existing approaches like Random Forest(RF),Naïve Bayes(NB),and the standard graph model.Various performance metrics like True Positive Rate(TPR),False Alarm Rate(FAR),Recall,Precision,F-measure,and ROC and run time analysis are evaluated to show the significance of the model.
