Host cardinality estimation is an important research field in network management and network security.The host cardinality estimation algorithm based on the linear estimator array is a common method.Existing algorithm...Host cardinality estimation is an important research field in network management and network security.The host cardinality estimation algorithm based on the linear estimator array is a common method.Existing algorithms do not take memory footprint into account when selecting the number of estimators used by each host.This paper analyzes the relationship between memory occupancy and estimation accuracy and compares the effects of different parameters on algorithm accuracy.The cardinality estimating algorithm is a kind of random algorithm,and there is a deviation between the estimated results and the actual cardinalities.The deviation is affected by some systematical factors,such as the random parameters inherent in linear estimator and the random functions used to map a host to different linear estimators.These random factors cannot be reduced by merging multiple estimators,and existing algorithms cannot remove the deviation caused by such factors.In this paper,we regard the estimation deviation as a random variable and proposed a sampling method,recorded as the linear estimator array step sampling algorithm(L2S),to reduce the influence of the random deviation.L2S improves the accuracy of the estimated cardinalities by evaluating and remove the expected value of random deviation.The cardinality estimation algorithm based on the estimator array is a computationally intensive algorithm,which takes a lot of time when processing high-speed network data in a serial environment.To solve this problem,a method is proposed to port the cardinality estimating algorithm based on the estimator array to the Graphics Processing Unit(GPU).Experiments on real-world high-speed network traffic show that L2S can reduce the absolute bias by more than 22%on average,and the extra time is less than 61 milliseconds on average.展开更多
Let Bn be the set of all n×n Boolean Matrices; R（A） denote the row space of A∈Bn, ｜R（A）｜ denote the cardinality of R（A）, m, n, k, l, t, i, γi be positive integers, Si, λi be non negative integers. In t...Let Bn be the set of all n×n Boolean Matrices; R（A） denote the row space of A∈Bn, ｜R（A）｜ denote the cardinality of R（A）, m, n, k, l, t, i, γi be positive integers, Si, λi be non negative integers. In this paper, we prove the following two results：（1）Let n≥13,n-3≥k〉Sl,Si＋〉Si,i=1,2…,l-1.if k＋l≤n,then for any m=2^k＋2^S1-l＋…＋2^S1,there exists A∈Bn,such that ｜R（A）｜=m.（2）Let n≥13,n-3≥k〉Sn-k-1〉Sn-k-2〉…S1〉λt〉λt-1〉…〉λ1,2≤t≤n-k.If exist γi（k＋1≤γi≤n-1,i=1,2…,t-1）γi〈γi＋1 and λt-λt-1≤k-Sn-γ1,λt-i-λt-i-1≤Sn-γi-Sn-γii＋1,i=1,2…,t-2,then for any m=2^k＋2^Sn-k-1＋2^Sn-k-1＋2^Sn-k-2＋…＋2^S1＋2^λt＋2^λt-1…＋2^λ1,there exists A∈Bn,as such that ｜R（A）｜=m.展开更多
In modern society,it is necessary to perform some secure computations for private sets between different entities.For instance,two merchants desire to calculate the number of common customers and the total number of u...In modern society,it is necessary to perform some secure computations for private sets between different entities.For instance,two merchants desire to calculate the number of common customers and the total number of users without disclosing their own privacy.In order to solve the referred problem,a semi-quantum protocol for private computation of cardinalities of set based on Greenberger-Horne-Zeilinger(GHZ)states is proposed for the first time in this paper,where all the parties just perform single-particle measurement if necessary.With the assistance of semi-honest third party(TP),two semi-quantum participants can simultaneously obtain intersection cardinality and union cardinality.Furthermore,security analysis shows that the presented protocol can stand against some well-known quantum attacks,such as intercept measure resend attack,entangle measure attack.Compared with the existing quantum protocols of Private Set Intersection Cardinality(PSI-CA)and Private Set Union Cardinality(PSU-CA),the complicated oracle operations and powerful quantum capacities are not required in the proposed protocol.Therefore,it seems more appropriate to implement this protocol with current technology.展开更多
The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’private information.Many intruders actively seek such private data either for sale...The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’private information.Many intruders actively seek such private data either for sale or other inappropriate purposes.Similarly,national and international organizations have country-level and company-level private information that could be accessed by different network attacks.Therefore,the need for a Network Intruder Detection System(NIDS)becomes essential for protecting these networks and organizations.In the evolution of NIDS,Artificial Intelligence(AI)assisted tools and methods have been widely adopted to provide effective solutions.However,the development of NIDS still faces challenges at the dataset and machine learning levels,such as large deviations in numeric features,the presence of numerous irrelevant categorical features resulting in reduced cardinality,and class imbalance in multiclass-level data.To address these challenges and offer a unified solution to NIDS development,this study proposes a novel framework that preprocesses datasets and applies a box-cox transformation to linearly transform the numeric features and bring them into closer alignment.Cardinality reduction was applied to categorical features through the binning method.Subsequently,the class imbalance dataset was addressed using the adaptive synthetic sampling data generation method.Finally,the preprocessed,refined,and oversampled feature set was divided into training and test sets with an 80–20 ratio,and two experiments were conducted.In Experiment 1,the binary classification was executed using four machine learning classifiers,with the extra trees classifier achieving the highest accuracy of 97.23%and an AUC of 0.9961.In Experiment 2,multiclass classification was performed,and the extra trees classifier emerged as the most effective,achieving an accuracy of 81.27%and an AUC of 0.97.The results were evaluated based on training,testing,and total time,and a comparative analysis with state-of-the-art studies proved the robustness and significance of the applied methods in developing a timely and precision-efficient solution to NIDS.展开更多
An edge coloring of hypergraph H is a function such that holds for any pair of intersecting edges . The minimum number of colors in edge colorings of H is called the chromatic index of H and is ...An edge coloring of hypergraph H is a function such that holds for any pair of intersecting edges . The minimum number of colors in edge colorings of H is called the chromatic index of H and is denoted by . Erdös, Faber and Lovász proposed a famous conjecture that holds for any loopless linear hypergraph H with n vertices. In this paper, we show that is true for gap-restricted hypergraphs. Our result extends a result of Alesandroni in 2021.展开更多
Up to now, the study on the cardinal number of fuzzy sets has advanced at on pace since it is very hard to give it an appropriate definition. Althrough for it in [1], it is with some harsh terms and is not reasonable ...Up to now, the study on the cardinal number of fuzzy sets has advanced at on pace since it is very hard to give it an appropriate definition. Althrough for it in [1], it is with some harsh terms and is not reasonable as we point out in this paper. In the paper, we give a general definition of fuzzy cardinal numbers. Based on this definition, we not only obtain a large part of results with re spect to cardinal numbers, but also give a few of new properties of fuzzy cardinal numbers.展开更多
文摘Host cardinality estimation is an important research field in network management and network security.The host cardinality estimation algorithm based on the linear estimator array is a common method.Existing algorithms do not take memory footprint into account when selecting the number of estimators used by each host.This paper analyzes the relationship between memory occupancy and estimation accuracy and compares the effects of different parameters on algorithm accuracy.The cardinality estimating algorithm is a kind of random algorithm,and there is a deviation between the estimated results and the actual cardinalities.The deviation is affected by some systematical factors,such as the random parameters inherent in linear estimator and the random functions used to map a host to different linear estimators.These random factors cannot be reduced by merging multiple estimators,and existing algorithms cannot remove the deviation caused by such factors.In this paper,we regard the estimation deviation as a random variable and proposed a sampling method,recorded as the linear estimator array step sampling algorithm(L2S),to reduce the influence of the random deviation.L2S improves the accuracy of the estimated cardinalities by evaluating and remove the expected value of random deviation.The cardinality estimation algorithm based on the estimator array is a computationally intensive algorithm,which takes a lot of time when processing high-speed network data in a serial environment.To solve this problem,a method is proposed to port the cardinality estimating algorithm based on the estimator array to the Graphics Processing Unit(GPU).Experiments on real-world high-speed network traffic show that L2S can reduce the absolute bias by more than 22%on average,and the extra time is less than 61 milliseconds on average.
基金Foundation item： Supported by the Guangdong Provincial Natural Science Foundation of China（06029035）
文摘Let Bn be the set of all n×n Boolean Matrices; R（A） denote the row space of A∈Bn, ｜R（A）｜ denote the cardinality of R（A）, m, n, k, l, t, i, γi be positive integers, Si, λi be non negative integers. In this paper, we prove the following two results：（1）Let n≥13,n-3≥k〉Sl,Si＋〉Si,i=1,2…,l-1.if k＋l≤n,then for any m=2^k＋2^S1-l＋…＋2^S1,there exists A∈Bn,such that ｜R（A）｜=m.（2）Let n≥13,n-3≥k〉Sn-k-1〉Sn-k-2〉…S1〉λt〉λt-1〉…〉λ1,2≤t≤n-k.If exist γi（k＋1≤γi≤n-1,i=1,2…,t-1）γi〈γi＋1 and λt-λt-1≤k-Sn-γ1,λt-i-λt-i-1≤Sn-γi-Sn-γii＋1,i=1,2…,t-2,then for any m=2^k＋2^Sn-k-1＋2^Sn-k-1＋2^Sn-k-2＋…＋2^S1＋2^λt＋2^λt-1…＋2^λ1,there exists A∈Bn,as such that ｜R（A）｜=m.
基金supported by the National Natural Science Foundation of China(61802118)Natural Science Foundation of Heilongjiang Province(YQ2020F013)supported by the Advanced Programs of Heilongjiang Province for the Overseas Scholars and the Outstanding Youth Fund of Heilongjiang University and the Heilongjiang University Innovation Fund(YJSCX2022-247HLJU)
文摘In modern society,it is necessary to perform some secure computations for private sets between different entities.For instance,two merchants desire to calculate the number of common customers and the total number of users without disclosing their own privacy.In order to solve the referred problem,a semi-quantum protocol for private computation of cardinalities of set based on Greenberger-Horne-Zeilinger(GHZ)states is proposed for the first time in this paper,where all the parties just perform single-particle measurement if necessary.With the assistance of semi-honest third party(TP),two semi-quantum participants can simultaneously obtain intersection cardinality and union cardinality.Furthermore,security analysis shows that the presented protocol can stand against some well-known quantum attacks,such as intercept measure resend attack,entangle measure attack.Compared with the existing quantum protocols of Private Set Intersection Cardinality(PSI-CA)and Private Set Union Cardinality(PSU-CA),the complicated oracle operations and powerful quantum capacities are not required in the proposed protocol.Therefore,it seems more appropriate to implement this protocol with current technology.
文摘The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’private information.Many intruders actively seek such private data either for sale or other inappropriate purposes.Similarly,national and international organizations have country-level and company-level private information that could be accessed by different network attacks.Therefore,the need for a Network Intruder Detection System(NIDS)becomes essential for protecting these networks and organizations.In the evolution of NIDS,Artificial Intelligence(AI)assisted tools and methods have been widely adopted to provide effective solutions.However,the development of NIDS still faces challenges at the dataset and machine learning levels,such as large deviations in numeric features,the presence of numerous irrelevant categorical features resulting in reduced cardinality,and class imbalance in multiclass-level data.To address these challenges and offer a unified solution to NIDS development,this study proposes a novel framework that preprocesses datasets and applies a box-cox transformation to linearly transform the numeric features and bring them into closer alignment.Cardinality reduction was applied to categorical features through the binning method.Subsequently,the class imbalance dataset was addressed using the adaptive synthetic sampling data generation method.Finally,the preprocessed,refined,and oversampled feature set was divided into training and test sets with an 80–20 ratio,and two experiments were conducted.In Experiment 1,the binary classification was executed using four machine learning classifiers,with the extra trees classifier achieving the highest accuracy of 97.23%and an AUC of 0.9961.In Experiment 2,multiclass classification was performed,and the extra trees classifier emerged as the most effective,achieving an accuracy of 81.27%and an AUC of 0.97.The results were evaluated based on training,testing,and total time,and a comparative analysis with state-of-the-art studies proved the robustness and significance of the applied methods in developing a timely and precision-efficient solution to NIDS.
文摘An edge coloring of hypergraph H is a function such that holds for any pair of intersecting edges . The minimum number of colors in edge colorings of H is called the chromatic index of H and is denoted by . Erdös, Faber and Lovász proposed a famous conjecture that holds for any loopless linear hypergraph H with n vertices. In this paper, we show that is true for gap-restricted hypergraphs. Our result extends a result of Alesandroni in 2021.
文摘Up to now, the study on the cardinal number of fuzzy sets has advanced at on pace since it is very hard to give it an appropriate definition. Althrough for it in [1], it is with some harsh terms and is not reasonable as we point out in this paper. In the paper, we give a general definition of fuzzy cardinal numbers. Based on this definition, we not only obtain a large part of results with re spect to cardinal numbers, but also give a few of new properties of fuzzy cardinal numbers.
