This research paper proposes a filter to remove Random Valued Impulse Noise (RVIN) based on Global Threshold Vector Outlyingness Ratio (GTVOR) that is applicable for real time image processing. This filter works with ...This research paper proposes a filter to remove Random Valued Impulse Noise (RVIN) based on Global Threshold Vector Outlyingness Ratio (GTVOR) that is applicable for real time image processing. This filter works with the algorithm that breaks the images into various decomposition levels using Discrete Wavelet Transform (DWT) and searches for the noisy pixels using the outlyingness of the pixel. This algorithm has the capability of differentiating high frequency pixels and the “noisy pixel” using the threshold as well as window adjustments. The damage and the loss of information are prevented by means of interior mining. This global threshold based algorithm uses different thresholds for different quadrants of DWT and thus helps in recovery of noisy image even if it is 90% affected. Experimental results exhibit that this method outperforms other existing methods for accurate noise detection and removal, at the same time chain of connectivity is not lost.展开更多
To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identific...To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental resuhs show that the improved SVM method-ICDRM does well at identification rate and training speed.展开更多
The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical field.It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which ...The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical field.It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which is essential in smart healthcare.However,Personal Health Records(PHRs)are normally kept in public cloud servers controlled by IoMT service providers,so privacy and security incidents may be frequent.Fortunately,Searchable Encryption(SE),which can be used to execute queries on encrypted data,can address the issue above.Nevertheless,most existing SE schemes cannot solve the vector dominance threshold problem.In response to this,we present a SE scheme called Vector Dominance with Threshold Searchable Encryption(VDTSE)in this study.We use a Lagrangian polynomial technique and convert the vector dominance threshold problem into a constraint that the number of two equal-length vectors’corresponding bits excluding wildcards is not less than a threshold t.Then,we solve the problem using the proposed technique modified in Hidden Vector Encryption(HVE).This technique makes the trapdoor size linear to the number of attributes and thus much smaller than that of other similar SE schemes.A rigorous experimental analysis of a specific application for privacy-preserving diabetes demonstrates the feasibility of the proposed VDTSE scheme.展开更多
[目的]DDoS攻击作为一种破坏性极强的网络威胁,严重影响电力系统的稳定运行。由于电力监控局域网中的数据流量复杂多变,DDoS攻击流量与正常流量在表现形式上存在较高相似性,导致二者难以有效区分。传统的静态阈值方法虽能在一定程度上...[目的]DDoS攻击作为一种破坏性极强的网络威胁,严重影响电力系统的稳定运行。由于电力监控局域网中的数据流量复杂多变,DDoS攻击流量与正常流量在表现形式上存在较高相似性,导致二者难以有效区分。传统的静态阈值方法虽能在一定程度上实现流量监测,但因无法适应流量的动态变化,常出现误判,从而削弱了对DDoS攻击的检测效果,难以为电力监控局域网提供可靠的安全保障。为此,提出一种基于动态阈值的电力监控局域网DDoS攻击检测方法。[方法]通过网络流量采集设备实时获取电力监控局域网的流量数据,并利用信息熵理论计算流量熵值。信息熵可反映数据的混乱程度:正常流量通常具有一定规律性,熵值相对稳定;而DDoS攻击流量因异常数据包的大量涌入,导致熵值显著波动。基于此特性,本文设定动态阈值,当流量熵值超过阈值时判定为异常流量。随后,提取异常流量的六元组特征集(包括平均流包数、平均字节数、源IP地址增速、流表生存时间变化、端口增速以及对流比),并将其输入预训练的最小二乘支持向量机(least squares support vector machine,LSSVM)分类器中。LSSVM通过对已知样本的学习建立特征与类别的映射关系,从而实现对异常流量的分类与判断,确定其是否为DDoS攻击流量。[结果]实验结果表明,本文方法在ROC曲线和PR曲线上均表现较好,ROC-AUC和PR-AUC值均较传统方法有所提高。这表明该方法在检测DDoS攻击时具备更高的准确率与召回率,能够有效识别隐藏于正常流量中的攻击流量,并显著降低误判率。[结论]基于动态阈值与LSSVM分类器的检测方法能够有效应对电力监控局域网中DDoS攻击与正常流量难以区分的问题,提升检测的准确性与可靠性,为电力监控局域网提供更为有效的DDoS攻击防护手段,有助于增强电力系统的安全性与稳定性,保障电力供应的可靠运行,对电力行业网络安全防护具有重要的实际应用价值。展开更多
文摘This research paper proposes a filter to remove Random Valued Impulse Noise (RVIN) based on Global Threshold Vector Outlyingness Ratio (GTVOR) that is applicable for real time image processing. This filter works with the algorithm that breaks the images into various decomposition levels using Discrete Wavelet Transform (DWT) and searches for the noisy pixels using the outlyingness of the pixel. This algorithm has the capability of differentiating high frequency pixels and the “noisy pixel” using the threshold as well as window adjustments. The damage and the loss of information are prevented by means of interior mining. This global threshold based algorithm uses different thresholds for different quadrants of DWT and thus helps in recovery of noisy image even if it is 90% affected. Experimental results exhibit that this method outperforms other existing methods for accurate noise detection and removal, at the same time chain of connectivity is not lost.
基金Sponsored by the National Natural Science Foundation of China(60472110)
文摘To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental resuhs show that the improved SVM method-ICDRM does well at identification rate and training speed.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61872289 and 62172266in part by the Henan Key Laboratory of Network Cryptography Technology LNCT2020-A07the Guangxi Key Laboratory of Trusted Software under Grant No.KX202308.
文摘The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical field.It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which is essential in smart healthcare.However,Personal Health Records(PHRs)are normally kept in public cloud servers controlled by IoMT service providers,so privacy and security incidents may be frequent.Fortunately,Searchable Encryption(SE),which can be used to execute queries on encrypted data,can address the issue above.Nevertheless,most existing SE schemes cannot solve the vector dominance threshold problem.In response to this,we present a SE scheme called Vector Dominance with Threshold Searchable Encryption(VDTSE)in this study.We use a Lagrangian polynomial technique and convert the vector dominance threshold problem into a constraint that the number of two equal-length vectors’corresponding bits excluding wildcards is not less than a threshold t.Then,we solve the problem using the proposed technique modified in Hidden Vector Encryption(HVE).This technique makes the trapdoor size linear to the number of attributes and thus much smaller than that of other similar SE schemes.A rigorous experimental analysis of a specific application for privacy-preserving diabetes demonstrates the feasibility of the proposed VDTSE scheme.
文摘[目的]DDoS攻击作为一种破坏性极强的网络威胁,严重影响电力系统的稳定运行。由于电力监控局域网中的数据流量复杂多变,DDoS攻击流量与正常流量在表现形式上存在较高相似性,导致二者难以有效区分。传统的静态阈值方法虽能在一定程度上实现流量监测,但因无法适应流量的动态变化,常出现误判,从而削弱了对DDoS攻击的检测效果,难以为电力监控局域网提供可靠的安全保障。为此,提出一种基于动态阈值的电力监控局域网DDoS攻击检测方法。[方法]通过网络流量采集设备实时获取电力监控局域网的流量数据,并利用信息熵理论计算流量熵值。信息熵可反映数据的混乱程度:正常流量通常具有一定规律性,熵值相对稳定;而DDoS攻击流量因异常数据包的大量涌入,导致熵值显著波动。基于此特性,本文设定动态阈值,当流量熵值超过阈值时判定为异常流量。随后,提取异常流量的六元组特征集(包括平均流包数、平均字节数、源IP地址增速、流表生存时间变化、端口增速以及对流比),并将其输入预训练的最小二乘支持向量机(least squares support vector machine,LSSVM)分类器中。LSSVM通过对已知样本的学习建立特征与类别的映射关系,从而实现对异常流量的分类与判断,确定其是否为DDoS攻击流量。[结果]实验结果表明,本文方法在ROC曲线和PR曲线上均表现较好,ROC-AUC和PR-AUC值均较传统方法有所提高。这表明该方法在检测DDoS攻击时具备更高的准确率与召回率,能够有效识别隐藏于正常流量中的攻击流量,并显著降低误判率。[结论]基于动态阈值与LSSVM分类器的检测方法能够有效应对电力监控局域网中DDoS攻击与正常流量难以区分的问题,提升检测的准确性与可靠性,为电力监控局域网提供更为有效的DDoS攻击防护手段,有助于增强电力系统的安全性与稳定性,保障电力供应的可靠运行,对电力行业网络安全防护具有重要的实际应用价值。
