In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In thi...In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In this paper,we propose a semi-supervised learning-based approach to detect malicious traffic at the access side.It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side,and also is free of labeled traffic data in model training.Specifically,we design a coarse-grained behavior model of Io T devices by self-supervised learning with unlabeled traffic data.Then,we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data.Experimental results show that our method can achieve the accuracy of 99.52%and the F1-score of 99.52%with only 1%of the labeled training data based on the CICDDoS2019 dataset.Moreover,our method outperforms the stateof-the-art supervised learning-based methods in terms of accuracy,precision,recall and F1-score with 1%of the training data.展开更多
With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT t...With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT terminal devices are also the important bottlenecks that would restrict the application of blockchain,but edge computing could solve this problem.The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity.However,user data in edge computing is usually stored and processed in some honest-but-curious authorized entities,which leads to the leakage of users’privacy information.In order to solve these problems,this paper proposes a location data collection method that satisfies the local differential privacy to protect users’privacy.In this paper,a Voronoi diagram constructed by the Delaunay method is used to divide the road network space and determine the Voronoi grid region where the edge nodes are located.A random disturbance mechanism that satisfies the local differential privacy is utilized to disturb the original location data in each Voronoi grid.In addition,the effectiveness of the proposed privacy-preserving mechanism is verified through comparison experiments.Compared with the existing privacy-preserving methods,the proposed privacy-preserving mechanism can not only better meet users’privacy needs,but also have higher data availability.展开更多
目的:探讨激素联合孟鲁司特对支气管哮喘患儿外周血中调节性T细胞表达的影响及临床疗效。方法:将80例支气管哮喘患儿随机分为常规治疗组和孟鲁司特组各40例。常规治疗组仅给予布地奈德混悬液0.5 mg/2 m L雾化吸入治疗,每次1吸,每天1次,...目的:探讨激素联合孟鲁司特对支气管哮喘患儿外周血中调节性T细胞表达的影响及临床疗效。方法:将80例支气管哮喘患儿随机分为常规治疗组和孟鲁司特组各40例。常规治疗组仅给予布地奈德混悬液0.5 mg/2 m L雾化吸入治疗,每次1吸,每天1次,连续治疗3个月。孟鲁司特组在此基础上加用孟鲁司特钠咀嚼片5 mg,1次/天,睡前口服,连用3个月。采用流式细胞术检测两组患儿治疗前后外周血中CD4+CD25+Foxp3+Treg表达水平,ELISA检测患儿血清中TGF-β1、白介素-10(IL-10)的蛋白表达水平,并进行日、夜间症状评分。结果:两组患儿治疗后日、夜间症状评分均较治疗前显著下降(P<0.05),但孟鲁司特组较常规治疗组下降更为明显(P<0.05);与治疗前相比,治疗3个月后孟鲁司特组和常规治疗组患儿Treg百分率、血清中TGF-β1和IL-10水平均有明显升高(P<0.05),且孟鲁司特组上述指标的升高水平较常规治疗组更显著(P<0.05)。结论:孟鲁司特能有效抑制哮喘的机制,有可能是通过上调哮喘患儿Treg比例,进而分泌表达TGF-β1、IL-10,抑制哮喘中的炎症反应,并显著改善哮喘患儿日、夜间症状评分。展开更多
基金supported in part by the National Key R&D Program of China under Grant 2018YFA0701601part by the National Natural Science Foundation of China(Grant No.U22A2002,61941104,62201605)part by Tsinghua University-China Mobile Communications Group Co.,Ltd.Joint Institute。
文摘In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In this paper,we propose a semi-supervised learning-based approach to detect malicious traffic at the access side.It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side,and also is free of labeled traffic data in model training.Specifically,we design a coarse-grained behavior model of Io T devices by self-supervised learning with unlabeled traffic data.Then,we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data.Experimental results show that our method can achieve the accuracy of 99.52%and the F1-score of 99.52%with only 1%of the labeled training data based on the CICDDoS2019 dataset.Moreover,our method outperforms the stateof-the-art supervised learning-based methods in terms of accuracy,precision,recall and F1-score with 1%of the training data.
文摘With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT terminal devices are also the important bottlenecks that would restrict the application of blockchain,but edge computing could solve this problem.The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity.However,user data in edge computing is usually stored and processed in some honest-but-curious authorized entities,which leads to the leakage of users’privacy information.In order to solve these problems,this paper proposes a location data collection method that satisfies the local differential privacy to protect users’privacy.In this paper,a Voronoi diagram constructed by the Delaunay method is used to divide the road network space and determine the Voronoi grid region where the edge nodes are located.A random disturbance mechanism that satisfies the local differential privacy is utilized to disturb the original location data in each Voronoi grid.In addition,the effectiveness of the proposed privacy-preserving mechanism is verified through comparison experiments.Compared with the existing privacy-preserving methods,the proposed privacy-preserving mechanism can not only better meet users’privacy needs,but also have higher data availability.
文摘目的:探讨激素联合孟鲁司特对支气管哮喘患儿外周血中调节性T细胞表达的影响及临床疗效。方法:将80例支气管哮喘患儿随机分为常规治疗组和孟鲁司特组各40例。常规治疗组仅给予布地奈德混悬液0.5 mg/2 m L雾化吸入治疗,每次1吸,每天1次,连续治疗3个月。孟鲁司特组在此基础上加用孟鲁司特钠咀嚼片5 mg,1次/天,睡前口服,连用3个月。采用流式细胞术检测两组患儿治疗前后外周血中CD4+CD25+Foxp3+Treg表达水平,ELISA检测患儿血清中TGF-β1、白介素-10(IL-10)的蛋白表达水平,并进行日、夜间症状评分。结果:两组患儿治疗后日、夜间症状评分均较治疗前显著下降(P<0.05),但孟鲁司特组较常规治疗组下降更为明显(P<0.05);与治疗前相比,治疗3个月后孟鲁司特组和常规治疗组患儿Treg百分率、血清中TGF-β1和IL-10水平均有明显升高(P<0.05),且孟鲁司特组上述指标的升高水平较常规治疗组更显著(P<0.05)。结论:孟鲁司特能有效抑制哮喘的机制,有可能是通过上调哮喘患儿Treg比例,进而分泌表达TGF-β1、IL-10,抑制哮喘中的炎症反应,并显著改善哮喘患儿日、夜间症状评分。
