A Heuristic Clustering Algorithm forIntrusion Detection Based on Information Entropy

This paper studied on the clustering problem for intrusion detection with the theory of information entropy, it was put forward that the clustering problem for exact intrusion detection based on information entropy is NP complete, therefore, the heuristic algorithm to solve the clustering problem for intrusion detection was designed, this algorithm has the characteristic of incremental development, it can deal with the database with large connection records from the internet.

Hierarchical and probabilistic quantum information splitting of an arbitrary two-qubit state via two cluster states

Based on non-maximally entangled four-particle cluster states, we propose a new hierarchical information splitting protocol to probabilistically realize the quantum state sharing of an arbitrary unknown two-qubit state. In this scheme, the sender transmits the two-qubit secret state to three agents who are divided into two grades with two Bell-state measurements,and broadcasts the measurement results via a classical channel. One agent is in the upper grade and two agents are in the lower grade. The agent in the upper grade only needs to cooperate with one of the other two agents to recover the secret state but both of the agents in the lower grade need help from all of the agents. Every agent who wants to recover the secret state needs to introduce two ancillary qubits and performs a positive operator-valued measurement（POVM） instead of the usual projective measurement. Moreover, due to the symmetry of the cluster state, we extend this protocol to multiparty agents.

Quantum Information Splitting of an Arbitrary Three-Qubit State by Using Cluster States and Bell States

簇状态的新应用为一个任意的 three-qubit 状态的切开的量信息(QIS ) 被调查。在我们的计划，一个 four-qubit 簇状态和一个贝尔状态被一个发送者(艾莉斯) 分享，一个控制器(查理) ，和接收装置(鲍勃) 。发送者和控制器仅仅需要执行钟状态大小(BSM ) ，接收装置能重建由在他知道发送者和控制器的测量结果以后，在他的 qubits 上执行一些适当地单一的转变州的任意的 three-qubit。这个 QIS 计划是确定的。

Probabilistic and Hierarchical Quantum Information Splitting Based on the Non-Maximally Entangled Cluster State

With the emergence of classical communication security problems,quantum communication has been studied more extensively.In this paper,a novel probabilistic hierarchical quantum information splitting protocol is designed by using a non-maximally entangled four-qubit cluster state.Firstly,the sender Alice splits and teleports an arbitrary one-qubit secret state invisibly to three remote agents Bob,Charlie,and David.One agent David is in high grade,the other two agents Bob and Charlie are in low grade.Secondly,the receiver in high grade needs the assistance of one agent in low grade,while the receiver in low grade needs the aid of all agents.While introducing an ancillary qubit,the receiver’s state can be inferred from the POVM measurement result of the ancillary qubit.Finally,with the help of other agents,the receiver can recover the secret state probabilistically by performing certain unitary operation on his own qubit.In addition,the security of the protocol under eavesdropping attacks is analyzed.In this proposed protocol,the agents need only single-qubit measurements to achieve probabilistic hierarchical quantum information splitting,which has appealing advantages in actual experiments.Such a probabilistic hierarchical quantum information splitting protocol hierarchical is expected to be more practical in multipartite quantum cryptography.

Simplified Four-Qubit Cluster State for Splitting Arbitrary Single-Qubit Information

简化 four-qubit 簇状态(即，(| 0000 +| 0011 +| 1100 -| 1111 )/2 ) 为切开一个任意的 single-qubit 量信息(QI ) 被探索。在为切开 tri 的 QI 发送者和接收装置之中的四 qubits 的各种各样的可行分布或切开双性人被发现。当其它简明地被提及时，为分发代表，相应切开计划和他们的 LOCC (本地操作和古典通讯) 充分地被介绍。

Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

Constructing global view with an ontology-based method for information sharing in the virtual organization

The problem of constructing global view of heterogeneous information sources for information sharing is becoming more and more important due to the availability of multiple information sources within the virtual organization. Global view is defined to provide a unified representation of the information in the different sources by analyzing concept schemas associated with them and resolving possible semantic heterogeneity. An ontology-based method for global view construction is proposed. In the method, （ 1 ） Based on the formal ontologies, the concept of semantic affinity is introduced to assess the level of semantic relationship between information classes from different information sources; （2） Information classes are classified by semantic affinity levels using clustering procedures so that their different representations can be analyzed for unification; （3） Global view is constructed starting from selected clusters by unifying representation of their elements. The application example of using the method in the joint-aerial defense organization is illustrated and the result shows that the proposed method is feasible.

Design of Distributed Authentication Mechanism for Equipment Support Information Network

Considering the secure authentication problem for equipment support information network,a clustering method based on the business information flow is proposed. Based on the proposed method,a cluster-based distributed authentication mechanism and an optimal design method for distributed certificate authority( CA)are designed. Compared with some conventional clustering methods for network,the proposed clustering method considers the business information flow of the network and the task of the network nodes,which can decrease the communication spending between the clusters and improve the network efficiency effectively. The identity authentication protocols between the nodes in the same cluster and in different clusters are designed. From the perspective of the security of network and the availability of distributed authentication service,the definition of the secure service success rate of distributed CA is given and it is taken as the aim of the optimal design for distributed CA. The efficiency of providing the distributed certificate service successfully by the distributed CA is taken as the constraint condition of the optimal design for distributed CA. The determination method for the optimal value of the threshold is investigated. The proposed method can provide references for the optimal design for distributed CA.

The hot issues of studies in China on digital information resources: Based on co-word analysis

With the SPSS and the help of factor method and hierarchical clustered method,journal articles on digital information resources(DIR) from CNKI in the past ten years are analyzed with a co-word analytical method in this paper. The hot issues of studies on DIR and the relationship between those subjects are analyzed in this investigation as well.

Information diffusion on adaptive network

Based on the adaptive network, the feedback mechanism and interplay between the network topology and the diffusive process of information are studied. The results reveal that the adaptation of network topology can drive systems into the scale-free one with the assortative or disassortative degree correlations, and the hierarchical clustering. Meanwhile, the processes of the information diffusion are extremely speeded up by the adaptive changes of network topology.

Intelligent Biometric Information Management

We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.

Research on the Application of Wireless Network in Collecting Road Traffic Information

Due to the characteristics of variability and dispersion in traffic information, to get the reliable real-time traffic information has been a bottleneck in the development of intelligent transportation systems. However, with the development of wireless network technology and mobile Internet, the mobile phones are rapidly developed and more popular, so it is possible to get road traffic information by locating the mobile phones in vehicles. The system structure for the road traffic information collection is designed based on wireless network and mobile phones in vehicles, and the vehicle recognition and its information computation methods are given and discussed. Also the simulation is done for vehicle recognition and computation based on fuzzy cluster analysis method and the results are obtained and analyzed.

融合多元时空信息的Informer-AD大坝变形预测模型

针对大坝变形时间序列预测问题,考虑多测点变形相关性,建立变形量时空多维输入矩阵,提出一种基于K-means聚类融合多元时空信息的Informer-AD大坝变形预测模型。首先,采用K-means聚类对变形测点进行分区;其次,引入面板数据回归模型分析分区结果;最后,提出融合多元时空信息的Informer-AD大坝变形预测模型。利用该模型对空间特征序列进行学习,通过全连接层整合空间特征,输出预测的大坝变形值。将上述预测模型运用于CT混凝土重力坝,结果表明,本文所提出的考虑时空关联性的预测方法充分挖掘大坝变形整体性态与测点空间分布特性的关系,能够更好地捕捉变形时空特性,进而提高预测精度。

Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is increasing.One of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise(DBSCAN).Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical data.In this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN algorithm.The existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the algorithm.To solve this problem,we developed the Genetic algorithm-based Keyword Matching DBSCAN(GKM-DBSCAN)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each keyword.In order to improve the performance of GKM-DBSCAN,we improved the general genetic algorithm by performing a genetic operation in groups.We conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force method.The experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 times.GKM-DBSCAN with the index number binarization(INB)is 1.8 times faster than GKM-DBSCAN with the cluster number binarization(CNB).

基于五粒子Cluster态的受控双向量子隐形传态

提出了一个由三方共享五粒子Cluster态为量子通道进行单粒子双向隐形传送的方案。方案中通信双方Alice和Bob各拥有五粒子Cluster态中的2个粒子以及未知单粒子态的粒子A和B,控制者Cindy拥有Cluster态中1个粒子。Alice和Bob首先分别对手中的粒子A,B以及通道中的1个粒子做Bell基测量。并通过经典信道将测量结果告知对方以及控制者Cindy。接着,Cindy对手中的粒子做单粒子投影测量,并通过经典信道告知Alice和Bob测量结果。Alice和Bob根据三方的测量结果分别对手中的另一个粒子做出相应的幺正操作得到对方所要传送的量子态,使单粒子态的受控双向隐形传态的目的得以实现。

Fuzzy c-means clustering with non local spatial information for noisy image segmentation

As an effective image segmentation method, the standard fuzzy c-means （FCM） clustering algorithm is very sensitive to noise in images. Several modified FCM algorithms, using local spatial information, can overcome this problem to some degree. However, when the noise level in the image is high, these algorithms still cannot obtain satisfactory segmentation performance. In this paper, we introduce a non local spatial constraint term into the objective function of FCM and propose a fuzzy c- means clustering algorithm with non local spatial information （FCM_NLS）. FCM_NLS can deal more effectively with the image noise and preserve geometrical edges in the image. Performance evaluation experiments on synthetic and real images, especially magnetic resonance （MR） images, show that FCM NLS is more robust than both the standard FCM and the modified FCM algorithms using local spatial information for noisy image segmentation.

Group decision-making method based on entropy and experts cluster analysis

According to the aggregation method of experts＇ evaluation information in group decision-making,the existing methods of determining experts＇ weights based on cluster analysis take into account the expert＇s preferences and the consistency of expert＇s collating vectors,but they lack of the measure of information similarity.So it may occur that although the collating vector is similar to the group consensus,information uncertainty is great of a certain expert.However,it is clustered to a larger group and given a high weight.For this,a new aggregation method based on entropy and cluster analysis in group decision-making process is provided,in which the collating vectors are classified with information similarity coefficient,and the experts＇ weights are determined according to the result of classification,the entropy of collating vectors and the judgment matrix consistency.Finally,a numerical example shows that the method is feasible and effective.

HTDet:A Clustering Method Using Information Entropy for Hardware Trojan Detection

Hardware Trojans(HTs)have drawn increasing attention in both academia and industry because of their significant potential threat.In this paper,we propose HTDet,a novel HT detection method using information entropybased clustering.To maintain high concealment,HTs are usually inserted in the regions with low controllability and low observability,which will result in that Trojan logics have extremely low transitions during the simulation.This implies that the regions with the low transitions will provide much more abundant and more important information for HT detection.The HTDet applies information theory technology and a density-based clustering algorithm called Density-Based Spatial Clustering of Applications with Noise(DBSCAN)to detect all suspicious Trojan logics in the circuit under detection.The DBSCAN is an unsupervised learning algorithm,that can improve the applicability of HTDet.In addition,we develop a heuristic test pattern generation method using mutual information to increase the transitions of suspicious Trojan logics.Experiments on circuit benchmarks demonstrate the effectiveness of HTDet.

A New Clustering Algorithm for Categorical Attributes

In traditional data clustering, similarity of a cluster of objects is measured by distance between objects. Such measures are not appropriate for categorical data. A new clustering criterion to determine the similarity between points with categorical attributes is pre- sented. Furthermore, a new clustering algorithm for categorical attributes is addressed. A single scan of the dataset yields a good clus- tering, and more additional passes can be used to improve the quality further.