2011-internetallics-Modeling hardness of polycrystalline materials and bulk metallic glasses

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基于人工免疫网络的联想记忆器

基于人工免疫网络的联想记忆器

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空间中都可 以产生新的最优方法。本文 中, 于这些最优方法简 基
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使用中继选择研究半双工非对称双向解码转发中继(IJCNIS-V3-N5-1)

使用中继选择研究半双工非对称双向解码转发中继(IJCNIS-V3-N5-1)
Index Terms—Two-way relaying, decode-and-forward, outage probability, relay selection, asymmetric traffic
I. INTRODUCTION
Cooperative relaying as a promising technology has attracted widespread attentions, since it can achieve higher transmission quality and throughput for wireless networks. In particular, considerable interests have focused on the half-duplex two-hop relaying, where two user terminals communicate with each other enabled by a relay node. Due to the half-duplex constraint, conventionally a direct four time-slot transmission scheme is employed for such a scenario to complete one round information exchange between the two source terminals. Although the conventional scheme is easier to implement, however, it results in a loss in spectrum efficiency due to the fact that more time slots are

BP神经网络与实例讲课文档

BP神经网络与实例讲课文档
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研究ANN方法
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第22页,共61页。
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a survey of visualizaiton systems for network security

a survey of visualizaiton systems for network security

A Survey of Visualization Systemsfor Network SecurityHadi Shiravi,Ali Shiravi,and Ali A.Ghorbani,Member,IEEE Abstract—Security Visualization is a very young term.It expresses the idea that common visualization techniques have been designed for use cases that are not supportive of security-related data,demanding novel techniques fine tuned for the purpose of thorough analysis.Significant amount of work has been published in this area,but little work has been done to study this emerging visualization discipline.We offer a comprehensive review of network security visualization and provide a taxonomy in the form of five use-case classes encompassing nearly all recent works in this area.We outline the incorporated visualization techniques and data sources and provide an informative table to display our findings.From the analysis of these systems,we examine issues and concerns regarding network security visualization and provide guidelines and directions for future researchers and visual system developers.Index Terms—Information visualization,network security visualization,visualization techniques.Ç1I NTRODUCTIONA LTHOUGH the visualization of network security events isthe subject of this survey,this paper does not focus on designing and developing a specific visualization system. Instead,we consider network security with respect to information visualization and introduce a collection of use-case classes.In this study,we provide an overview of the increasing relevance of security visualization.We explore a novel classification approach and review the artifacts most commonly associated with security visualization systems. We provide a historical context for this emerging practice and outline its surrounding concerns while providing design guidelines for future developments.Visual data analysis help to perceive patterns,trends, structures,and exceptions in even the most complex data sources.As the quantity of network audit traces produced each day grows exponentially,communicating with visuals allows for comprehension of these large quantities of data. Visualization allows the audience to identify concepts and relationships that they had not previously realized.There-by,explicitly revealing properties and relationships inher-ent and implicit in the underlying data.Identifying patterns and anomalies enlightens the user,provides new knowl-edge and insight,and provokes further explorations.It is these fascinating capabilities that influence the use of information visualization for network security.Visualiza-tion is not only efficient but also very effective at communicating information[1].A single graph or picture can potentially summarize a month’s worth of intrusion alerts(depending on the type of network),possibly showing trends and exceptions,as opposed to scrolling through multiple pages of raw audit data with little sense of the underlying events.Security Visualization is a very young term[2],[3].It expresses the idea that common visualization techniques have been designed for use cases that are not supportive of security-related data,demanding novel techniques fine tuned for the purpose of thorough analysis.It may not always be possible to fully predict how an end user will perceive and interpret a design due to the varying nature of the audience’s cognitive characteristics.Yet careful con-sideration of the user’s needs,cognitive skills,and abilities can determine the appropriate content and design.Often associated with human-computer interaction,the philoso-phy of user-centered design places the end user at the center of the design work security is a highly specialized and technical discipline and operation.It deals with packets and flows,intrusion detection and prevention systems,vulnerabilities,exploits,malware,honeypots,and risk management and threat mitigation.The complex, dynamic,and interdependent nature of network security demands extensive research during the development process.Without an in-depth understanding of security operations and extensive hands on experience,developing a security visualization system will not be possible.A design process centered on the needs,behaviors,and expectations of security analysts can greatly influence and impact the usability and practicality of such systems.For best results, security experts and visual designers must thereby colla-borate to complement each other’s skills and expertise to innovate informative,interactive,and exploratory systems that are technically accurate and aesthetically pleasing.In this survey,we begin by looking into different categories of data sources incorporated in the design of security visualizations and provide an informative list of sources accessible to the research community.We continue in Section3by expressing our main contribution in the classification of network security visualization systems. We provide a detailed description of the proposed taxonomy.The authors are with the Information Security Centre of Excellence,Faculty of Computer Science,University of New Brunswick,540WindsorStreet,Gillin Hall,Room E128Fredericton,NB E3B5A3,Canada.E-mail:{hadi.shiravi,ali.shiravi,ghorbani}@unb.ca.Manuscript received30Aug.2010;revised26June2011;accepted12Aug.2011;published online23Aug.2011.Recommended for acceptance by K.-L.Ma.For information on obtaining reprints of this article,please send e-mail to:tvcg@,and reference IEEECS Log Number TVCG-2010-08-0203.Digital Object Identifier no.10.1109/TVCG.2011.144.1077-2626/12/$31.00ß2012IEEE Published by the IEEE Computer Societytogether with an analysis of the derived use-case classes.We follow by giving a thorough description of each system as we outline its strengths and weaknesses.An overall assessment of systems in each use-case class in addition to guidelines and directions for future systems is also provided.We summarize the multiple attributes of recent network security visualization systems in a table for better future references. We continue in Section4by outlining issues and concerns surrounding security visualization by elaborating on seven potential pitfalls.We conclude this research in Section5by summarizing our findings.Papers studied in this survey were selected based on the following metrics:1.Relevance to network security:As the title of thepaper indicates,this study focuses specifically onnetwork security visualization systems.Visualiza-tions of code security,binary files,or visualcryptanalysis are subjects that could span anothervolume of similar size and are thereby not consid-ered in this study.2.Contribution of system and visual techniques:Dueto the chronological study of papers,systems thathave utilized a specific visualization technique ormethod with highly similar characteristics to thoseof previous systems have not been selected for thissurvey.Similarly,visualization systems that lackcontextual,perceptive,and cognitive considerationsare also not considered.3.Satisfactoriness of evaluation:Although most sys-tems surveyed in this paper lack formal evaluation,yet many have been validated through ad hoc use-case attack scenarios.Systems that lack even thisbasic validation strategy are also not considered inthis survey.We believe these three metrics impact the quantity and quality of papers surveyed in this work to resemble systems that are focused explicitly on network security,are novel in their incorporated visual techniques,and are validated on at least a use-case scenario.Systems that do not adhere to these metrics are thereby not considered in this study.2D ATA S OURCESVisualization cannot happen without data or information. Many of the systems surveyed in this paper have been created based on a single source of data.Looking at network events from multiple perspectives by incorporat-ing different data sources into a system can provide an analyst with a richer insight into the underlying events. Therefore,a nonexhaustive list of potential data sources that are available to the research community and may be incorporated in the design of network security visualization systems is given in Table1.The decision on the type and number of incorporated data sources and the set of extracted features from each data source is a critical act. The data sources mentioned in Table1are very generic and in some cases,e.g.,network traces,hundreds of features can be extracted from them.The importance of selecting the appropriate features,as a first step in designing a visualization system,has been extensively studied in theTABLE1Potential Data Sources for Security Visualizationsfields of statistics,pattern recognition,machine learning,and data mining and the resulting efforts have been applied to the fields of artificial intelligence,text categorization,and also intrusion detection.These studies are of great benefit to security visualization researchers as often the required steps of selecting an optimal subset of features (subset generation,subset evaluation,stopping criterion,and result validation)have been examined extensively before.Based on a particular problem a researcher is facing and the data sources available to him or her,a subset of features may be extracted and incrementally validated until a desired optimality is achieved.3C LASSIFICATION A PPROACHThe approach taken in many visualization systems is data driven.In network security for instance,one may take a single data source like packet traces and try to develop a visualization system based on that.The methodology behind the design of visualization systems should be use-case driven.A visualization system should be built to support answering specific questions.In this approach,the system may incorporate one or multiple data sources.Based on this mindset,we have classified the recent works of network security visualization into five use-case classes.We provide a detailed description of each class,discuss several recent examples of each approach,specify the incorporated visualization techniques of each system,and challenge the applicability of each use-case class in regard to modern day networks.Guidelines for future research,and directions for informative and efficient visuals are also provided for each use-case class at the end of each section.3.1Host/Server MonitoringIn this class of visualization,the main display is devoted to the representation of hosts and servers.The intent is to display the current state of a network by visualizing the number of users,system load,status,and unusual or unexpected host or server activities.Systems of this class should also be able to correlate communicating processes of a single host or server with the network traffic.This feature enhances the ability of a user to identify malware as they often manifest themselves in irregular and often anon-ymous system processes.The work of Erbacher et al.[4],[5]constitutes one of the earlier works in this class.As illustrated in Fig.1,hosts are arranged around five concentric circles with the monitored server placed in the center.The ring of a node depicts the difference between its IP address and that of the monitored system,resulting in hosts residing inside the local subnet to appear closest to the monitored system.The position of a host on the circular ring is also recorded to ensure that a specific host always appears in the same position.Multiple visual attributes are assigned to each node as they are depicted using glyphs.For the monitored server,for example,spokes extending from its perimeter represent the number of connected users.As connections are made from hosts to the monitored server and based on the connection type,communication links are shown with different line patterns.These visual illustrations give an analyst an exploratory framework to work with as itstrengthens her abilities to detect unknown relationships within the underlying data.Tudumi [6]is also one of the earlier systems belonging to this category aimed at monitoring and auditing user behavior on a server.In a 3D visualization,Tudumi visualizes connections using lines and system nodes using 3D glyphs as they are displayed on multilayered concentric disks.Similar to Erbacher’s system,Tudumi uses line patterns to encode different access methods including coarse dashed lines to represent a terminal service and thin dashed lines to represent file transfer.The previous two systems are more concerned with the activities of a single or a limited number of hosts or servers rather than incorporating a larger portion of the network.NVisionIP [7],[8]takes on a different approach.It represents an entire class B IP network on a single 256Â256matrix grid with each cell of the matrix representing interactions between the corresponding network hosts.In the galaxy view of the system,all network subnets are listed along the horizontal axis while hosts of each subnet are listed along the vertical axis.As the number of visualized elements increases,inevitably the portion of the screen allocated to each object decreases.NVisionIP uses a magnifier function to allow the user to hover over the display screen.If an analyst is interested in a particular part of the display,she can select it using the magnifier function.A bar graph is then displayed for each host,depicting their activity over common and uncommon port numbers.Portall [9]digs deeper into the monitored hosts and tends to correlate TCP connections with the host processes that generate them,allowing an end-to-end visualization of communications between distributed processes.As dis-played in Fig.2,the main display consists of two parallel axes with the left side representing clients and the right side representing servers and their respective processes.A line is drawn from a client to a server to depict a TCP connection.Portall is one of the first systems that visually correlatesSHIRAVIET AL.:A SURVEY OF VISUALIZATION SYSTEMS FOR NETWORK SECURITY 1315Fig.1.Basic visual representation of network and system activity in [4].network traffic to host processes,allowing spywares and ad-wares to be easily detected.Similar in nature to Portall is the Host Network(HoNe) [10]visualization system that also visualizes communicat-ing processes of a host with network traffic.The authorsargue that the reason behind not being able to correlate processes to network traffic is inherent in the design of the TCP/IP networking model of modern operating systems. The system visualizes client side hosts and their respective processes and port numbers on the left side of the display while external sources and their respective port numbers are displayed on the right side.Different to Portall,HoNe uses splines rather than simple straight lines to connect processes of a client to external servers.Perlman and Rheingans[11]extend existing approaches in host/server monitoring by adding and encoding service and temporal information inside the visualized node itself. Each host inside the network is illustrated using a circular glyph node much like a pie chart.Each glyph represents the existence and amount of activity for a particular service. The size of the glyph represents the total amount of activity of the node,measured by the number of packets.Wedge sizes identify what percentage of the total activity belongs to a particular service.A collection of different colors is used to distinguish between different services of a host.The system also incorporates time by using a stacked pie chart approach where the most outer ring represents the most recent time slice.Hosts are laid out in a simple node-link layout with straight lines connecting the communicating hosts together.The Radial Traffic Analyzer[12]visualizes the distribu-tion of network traffic of a particular host using a radial representation.The system is composed of four concentric circles,each mapped to an attribute of the underlying data. In its default setting,the innermost ring is assigned to source IP addresses,the second ring to destination IP addresses,and the third and fourth rings are mapped to source and destination port numbers,respectively.The notion of assigning port numbers to services and applica-tions,devised in this system,is no longer accurate in modern networks as many applications tend to piggyback or tunnel through common port numbers such as HTTP(80) and HTTPS(443).The work of Mansmann et al.[13]is one of the recent works in this class.In their proposed visual analytics tool, by incorporating a force directed graph layout,host behavior is monitored and irregular positional changes are flagged as suspicious.The authors believe that change in network traffic over time is well suited for detecting uncommon system behaviors.As illustrated in Fig.3,in the first step of the visualization,a set of dimension nodes,each representing a network service are laid out using a circular force directed layout.In the second step,the observation nodes representing a particular host are placed on the display and are connected to their corresponding dimen-sion nodes through virtual springs.Node size is calculated based on the sum of transferred bytes using a logarithmic scale.Since the state of a monitored host is displayed through multiple time stamps and due to the large number of visualized elements on the display,depicting multiple hosts without overlap is a challenge for this visual.Overall assessment of the host/server monitoring class. The ability of the visualization systems of this class in displaying a restrained number of hosts or servers within the monitored network is a perceptible issue.Most,if not all, of the systems of this class are constrained by their incorporated visualization techniques.As networks tend to grow in size and complexity at an exponential rate,there is an unprecedented need to create meaningful contexts.Even the smallest of university campus networks can consist of thousand of hosts,with which the aforementioned systems are less than capable of displaying in a clear and perceivable manner.For an analyst,simpler graphics are easier to understand and interpret than complex ones,since complex-ity can often influence the ability of the viewer in perceiving and decoding a visual.The overwhelming number of hosts in a monitored network,accompanied by the many hundreds of events generated for each,and the complexity of relations between events limit the cognitive process of situation awareness for analysts.For visualizations of this class to be effective and to clearly convey meaning,it is essential for them to devise an automated process that prioritizes situations and projects critical events.If a visualization system,due to its incorporated visualization technique,is limited in displaying a comprehensible range of hosts,envisioning a situation assessment process is inevitable.In this case,the process of identifying hosts with anomalous behavior and the mechanism of correlating1316IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,VOL.18,NO.8,AUGUST2012Fig.2.A screen shot of Portall[9]with monitored hosts and servers stacked on the left and rightsides.Fig.3.A sketch showing the coordinate calculation of a host position at aparticular point of time as depicted in[13].events is partly undertaken in a separate background component,and the processed results are projected to the visualization system.In this manner,the load on the visualization system is reduced considerably;allowing for a near real time analysis of events and a more responsive system.Packet traces,server logs,and network flows constitute primary data sources for this class of visualiza-tions.Node link graphs,glyphs,and scatter plots are also primary visualization techniques incorporated in this class.3.2Internal/External MonitoringVisualizations of this class are concerned with the interac-tion of internal hosts with respect to external IPs.Similar to the above-mentioned class,this class of visualization also incorporates a display of internal hosts,but in relation to communicating external IPs.Since the art of displaying internal hosts in a nonoccluding and meaningful manner is by itself a delicate act,adding the burden of displaying hundreds and thousands of external IPs is a nontrivial process for systems of this class.VISUAL [14]is one of the earliest systems of this class.It is a security visualization system with the goal of allowing an analyst to see communication patterns between an internal network in regard to external sources.As displayed in Fig.4,the internal network is represented by a grid with each cell depicting one of the internal hosts.External sources are represented as squares outside the internal grid with the square size denoting the level of activity.Simple straight lines are used to represent a connection between internal and external hosts.Multiple filtering mechanisms can be used to filter out internal or external hosts leading to a less cluttered display.Various detailed information regarding a host can also be displayed upon user request.VizFlowConnect [15]uses parallel axes to display net-work traffic between internal and external hosts.The goal of the system is to display relationships between communicat-ing machines of a network.The main display consists ofthree distinct parallel axes.The center axis represents internal hosts.The left axis corresponds to machines originating network traffic to the internal network while the right axis represents the destination machines of internal traffic.Each point on an axis represents an IP address and connections between points on parallel axes represent network communication.Time is incorporated in the system by using animation and various multiple views allow for further exploratory analysis.VizFlowConnect also shows individual host statistics,but further drill down depth is desired.Erbacher et al.[16]have come up with a second visualization system;this time aimed at internal/external host monitoring and geared toward filtering unwanted data,allowing focus on more critical events.The visual system incorporates a radial panel design consisting of multiple concentric disks each showing a constant period of time.Local IP addresses are placed around the radial disks while remote hosts are located on the top and bottom of the display.In order to avoid overlapping lines,an IP address located on the top half of the circle is connected to remote hosts located along the top of the display while hosts located on the bottom half of the circle are connected to remote hosts located on the bottom of the display.In the same manner,port numbers are also allocated on the left and right sides.The outer ring of the display shows the most recent period with interior rings displaying previous periods.This feature allows an analyst to see trends and patterns within the communicating hosts.Hosts are identified by dots on the circular rings resulting in difficult user interaction.In a visual network traffic analysis system,TNV [17],Goodall et al.believe that analysts often lose sight of the big picture while examining low-level details of attacks.In order to prevent this loss of context,they propose TNV with the goal of providing a focused view on packet level data in the high-level network traffic context.As illustrated in Fig.5,the main visual component of TNV is a matrix displayingSHIRAVI ETAL.:A SURVEY OF VISUALIZATION SYSTEMS FOR NETWORK SECURITY 1317Fig.4.VISUAL [14]displaying 80hours of network data on a network of 1,020hosts.Fig. 5.TNV [17]showing 50,000network packets in a 90minute time span.network activity of hosts over time,with connections between hosts overlaid on the matrix.TNV is designed based on a focus and context paradigm where the center of the display,the focal area,shows communicating hosts within wider columns.In order to preserve continuity throughout the display,the context area,located to the left and right sides of the display,has gradually decreasing width.Each host inside the matrix is colored according to its level of activity and multiple linked views are used to illustrate port activity and details of raw packets.TNV is one of the few security visualization systems that has been fully implemented and is freely available for download.Overall assessment of the internal/external monitoring class.Similar to the recommendations mentioned for the host/server monitoring class,the visualizations of this class can greatly benefit from a situation assessment component.This component can be defined in two different styles.One,as a process that automatically identifies and evaluates the impact of underlying events and relates them to assets of the monitored network or two,as an exploratory system that provides the facility for an analyst to validate various hypotheses.In the first style,due to the processing of events in a background component,the visual component can focus better toward richer and more responsive user interfaces.A necessity that is lacking and often overlooked in security visualization systems.In the second style,it is the analyst’s job to pose queries,correlate disparate events,and derive insightful meanings from the visualization.These activities impose the need for visual exploration and filtering mechanisms to be implemented.Dynamic queries,details on demand techniques,and linking and brushing interaction techniques are essential concepts that need to be addressed and considered in this class of visualizations.Color maps,radial panels,scatter plots,and parallel coordinates are common visual techniques used in this class.Packet traces and network flows are also used as the main data sources for visualizations of this class.3.3Port ActivityDesigners of this class of visualization argue that various malicious programs like viruses,Trojans,and worms manifest themselves through unusual and irregular port activity.Visualizations of this class can aid in the detection of malicious software running inside a network.Scaling techniques must be incorporated in the design of visualiza-tions of this class,due to the amount of traffic as well as the large range of possible port numbers and IP addresses.One of the earlier visualization systems designed speci-fically for this class is the work of Abdullah et al.[18].In their developed system,a port-based overview of network activity is presented through stacked histograms of aggre-gated port activity.The authors believe that port activity can be used to detect zero-day exploits that are not detectable by conventional methods.As displayed in Fig.6,port numbers are aggregated into multiple groups based on the services provided in the network.Well-known ports (<1;024)are assigned to major services on a system making them more vulnerable to attacks.For this reason,they are placed into bins of 100’s,registered ports (<50;000)are placed into bins of 10,000’s,and the remaining private/dynamic ports (50,000-65,535)are placed into a single bin.Color andscaling methods are also used effectively to distinguish between the aggregated port groups.In their developed system,the user has the ability to drill down in order to view finer details of the visualization.Displaying data over time also helps to highlight any patterns or trends appearing in irregular activities.The visualization is intuitive,easy to work with,and meets its intended design goals.The Spinning Cube of Potential Doom [19]is an interesting example of security visualization.A system that visualizes real-time port and IP data in a three dimensional cube,displayed as a rotating scatter plot.Each axis of the 3D display represents a component of a TCP connection.Destination IP addresses are mapped to the X -axis,port numbers to the Y -axis,and source IP addresses to the Z -axis.TCP connections are displayed as individual dots with color used to distinguish a successful connection from an unsuccessful one.Time is displayed through the use of animation.While quite useful to see coarse trends in large-scale networks,it lacks drill down mechanisms,multiple views,and interactive capabilities.The system is good for solo attacks and can only be used for port scan detection.PortVis [20]employs a colored-based grid visualization to map network activity to cells of a grid.As depicted in Fig.7,the main display contains a 256Â256grid where each point represents one of the possible 65,536port numbers.The location of a port on the gird is determined by breaking the port number into a 2-byte (X,Y)location.X being the high byte of the port number and Y being the low byte.Changes and variations of each point,with respect to time,is depicted using color.Black portrays no variation or change,blue depicts a small level of variance,red refers to a larger level of variance,while white denotes the most variant.The grid can be magnified to provide further detailed information about specific ports.A drawback of the system is seen when a port with suspicious activity is located among a collection of ports with a high,legitimate,level of activity.In this case,the ability to identify and focus on that region is not an easy Bytes Viewer [21]allows a detailed inspection of the behavior of an individual host over time.It facilitates in identifying behavioral changes that manifest themselves as unusual port usage or traffic volume regarding a single Bytes offers multiple views in both two and three dimensions,making it possible for an analyst to view the1318IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,VOL.18,NO.8,AUGUST2012Fig.6.Botnet traffic capture displayed using a cube root scale histogram in [18].。

信息网络安全-7-第7章 数字签名(研究生)

信息网络安全-7-第7章 数字签名(研究生)
对整体消息进行签名;
对压缩的消息进行签名。
按照消息/签名的对应关系划分
确定性( deterministic )数字签名:消息与签名一一对应, 对同一消息的签名永不变化,如RSA和Rabin算法;
随机化(randomized)或概率式数字签名:对同一消息的签 名是变化的。因此,此类签名取决于算法中的随机参数的取 值,如ElGamal算法。
DSS签名算法的实现速度
DSA
密钥生成 预计算 14s 14s
RSA
Off card N/A
证明: v=[(gu1yu2) mod p] mod q =[g H(m)wyrw mod p] mod q =[g H(m)wgxrw mod p] mod q =[g[H(m)+xr]w mod p ] mod q 而: [H(m)+xr]w = [H(m)+xr]s-1=k mod q 所以:v=gk mod q= r
计算:w’=gsy-r mod p
计算:H(w’||m)
验证H(w’||m)=r ?即 Very{(r, s), m}=真
Schnorr签名体制的安全性
Schnorr与ElGamal的区别
在 ElGamal体制中, g为Zp的本原元素;在 Schnorr体制中, g为Zp*中的子集Zq*的本原元,它不是Zp* 的本原元。 Schnorr的签名长度要比ElGamal短,由|q|及|H(m)|决定。 w=gk mod p可以预先计算,签名只需1次乘法和1次加法, 所以签名速度非常快,适用于智能卡应用。
对于课件中出现的缺点和错误,欢迎读者提 出宝贵意见,以便及时修订。
课件制作人:刘建伟 2016年10月25日
第7章 数字签名

基于定性微分博弈的通信网络攻击预警方法

基于定性微分博弈的通信网络攻击预警方法

DCWTechnology Study技术研究23数字通信世界2023.06通信网络在广义上指的是在各个孤立的通信设备之间建立物理连接,形成信息交换链路,通过链路,实现网络资源实时共享与高效通信的目标[1]。

通常情况下,通信网络由传输、交换与终端设备共同组成,能够为各个行业领域提供通信服务[2-3]。

在通信网络运行过程中,其环境条件较为复杂[4]。

由于受到网络干扰因素的影响,整体网络防御能力较低,无法高效地应对各类复杂多变的网络攻击,从而无法保证通信网络的安全性[5]。

因此,开发高效的通信网络攻击预警方法至关重要。

为了提高预警方法在实际应用中的性能优势,本文特引入定性微分博弈原理,提出一种全新的通信网络攻击预警方法。

通过定性微分博弈模型的连续决策作用,划分通信网络安全状态空间,有针对性地预测各个状态空间的攻击与威胁状态演化情况,为网络攻击预警提供精确的数据支持,提高预警结果的准确性。

1 通信网络攻击预警方法1.1 建立通信网络安全状态演化分析模型本文建立的通信网络安全状态演化分析模型中包含4种不同的节点状态,分别为正常状态、攻击感染状态、攻击修复状态以及攻击失效状态[6-7]。

各个网络节点之间通过状态转移,反映网络安全状态。

模型中通信网络节点状态转移如图1所示。

基于定性微分博弈的通信网络攻击预警方法程书红(闽南理工学院,福建 石狮 362700)摘要:通信网络攻击预警方法覆盖面不足,无法预警位于通信网络隐藏区内的攻击情况,导致通信网络攻击预警结果准确率较低。

文章提出基于定性微分博弈的通信网络攻击预警方法。

首先,建立通信网络安全状态演化分析模型,获取网络运行的实时工况以及存在攻击威胁的路径。

其次,基于定性微分博弈原理,划分通信网络攻击多维状态空间,全面设计网络攻击预警算法。

最后,在此基础上,建立网络攻击预警指标体系,设计预警等级。

实验结果表明,应用新的预警方法后,预警精度优势显著,准确率达到95%以上。

社会化问答平台的答案质量评估_以_知乎_百度知道_为例_贾佳

社会化问答平台的答案质量评估_以_知乎_百度知道_为例_贾佳
以 信 息 为 中 心 的 研 究 ,主 要 包 括 围 绕 答 案 展开的答案质量评估和用 户 满 意 度 研 究、以 及 问题推荐和答案检索等,其 中 答 案 质 量 评 估 和 用户 满 意 度 是 目 前 国 外 研 究 的 热 点。Agich- tein等将文本特征、用 户 关 系 以 及 使 用 统 计 特 征,输入到一个分类器中产 出 对 答 案 质 量 评 价 的结果 。 [3] 而 Shah发现人 工 标 注 者 之 间 对 相 同数据集的评分有很高的相关性。他们采用 基于人工打 分 的 多 维 度 模 型,从 原 创 性、简 洁 性、相关性等 13 个 维 度 来 预 测 雅 虎 的 最 佳 答 案[4]。Kim 和 Oh根据提问 者 的 评 论 总 结 了 判 断最佳答案的评价标准,并 提 炼 出 一 个 分 析 框 架,其中包括内容价值、认 知 价 值、社 会 情 感 价 值、信 息 源 价 值、外 在 价 值、效 用,并 分 析 了 各 个标准的 影 响 因 素[5]。Jeon 等 根 据 社 区 问 答
在用 户 满 意 度 研 究 方 面,Agichtein 等 以 Yahoo!Answers数据 为 样 本,使 用 分 类 算 法 预 测用 户 对 答 案 的 满 意 度,并 比 较 了 决 策 树、 SVM、叶 贝 斯 等 分 类 器 在 预 测 用 户 满 意 度 中 的 不 同 [11]。Shah以 雅 虎 知 识 堂 为 对 象 ,研 究 发 现 超 过 30% 的 问 题 在 5 分 钟 之 内 可 以 收 到 回 答 , 92% 的 问 题 在 一 小 时 之 内 可 以 收 到 回 答 。 但 最 佳问题的回复时间往往会更久 。 [12]
Jia Jia Song Enmei Su Huan

Globally networked risks and how to respond

Globally networked risks and how to respond
Many disasters in anthropogenic systems should not be seen as ‘bad luck’, but as the results of inappropriate interactions and institutional settings. Even worse, they are often the consequences of a wrong understanding due to the counter-intuitive nature of the underlying system behaviour. Hence, conventional thinking can cause fateful decisions and the repetition of previous mistakes. This calls for a paradigm shift in thinking: systemic instabilities can be understood by a change in perspective from a component-oriented to an interaction- and network-oriented view. This also implies a fundamental change in the design and management of complex dynamical systems.
BOX 1
Risk, systemic risk and hyper-risk
According to the standard ISO 31000 (2009; /iso/ catalogue_detail?csnumber543170), risk is defined as ‘‘effect of uncertainty on objectives’’. It is often quantified as the probability of occurrence of an (adverse) event, times its (negative) impact (damage), but it should be kept in mind that risks might also create positive impacts, such as opportunities for some stakeholders.
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Modeling hardness of polycrystalline materials and bulk metallic glassesXing-Qiu Chen *,Haiyang Niu,Dianzhong Li *,Yiyi LiShenyang National Laboratory for Materials Science,Institute of Metal Research,Chinese Academy of Sciences,Wenhua Road No.72,Shenyang 110016,Liaoning,PR Chinaa r t i c l e i n f oArticle history:Received 24February 2011Accepted 23March 2011Available online 2June 2011Keywords:E.Mechanical properties,theory B.Elastic propertiesa b s t r a c tThough extensively studied,hardness,de fined as the resistance of a material to deformation,still remains a challenging issue for a formal theoretical description due to its inherent mechanical complexity.The widely applied Teter ’s empirical correlation between hardness and shear modulus has been considered to be not always valid for a large variety of materials.The main reason is that shear modulus only responses to elastic deformation whereas the hardness links both elastic and permanent plastic properties.We found that the intrinsic correlation between hardness and elasticity of materials correctly predicts Vickers hardness for a wide variety of crystalline materials as well as bulk metallic glasses (BMGs).Our results suggest that,if a material is intrinsically brittle (such as BMGs that fail in the elastic regime),its Vickers hardness linearly correlates with the shear modulus (H v ¼0.151G ).This correlation also provides a robust theoretical evidence on the famous empirical correlation observed by Teter in 1998.On the other hand,our results demonstrate that the hardness of polycrystalline materials can be correlated with the product of the squared Pugh ’s modulus ratio and the shear modulus (H v ¼2ðk 2G Þ0:585À3where k ¼G /B is Pugh ’s modulus ratio).Our work combines those aspects that were previously argued strongly,and,most importantly,is capable to correctly predict the hardness of all hard compounds known included in several pervious models.Ó2011Elsevier Ltd.All rights reserved.1.IntroductionDespite the great efforts,to understand the theory of hardness and to design new ultrahard materials are still very challenging for materials scientists [1e 4].During the past few years,several semi-empirical theoretical models [5e 9]have been developed to estimate hardness of materials based on:(i )the bond length,charge density,and ionicity [5],(ii )the strength of the chemical bonds [6],(iii )the thermodynamical concept of energy density per chemical bonding [7],and (iv )the connection between the bond electron-holding energy and hardness through electronegativity [8],and (v )the temperature-dependent constraint theory for hardness of multicomponent bulk metallic glasses (BMGs)[9].Experimentally,hardness is a highly complex property since the applied stress may be dependent on the crystallographic orientations,the loading forces and the size of the indenters.In addition,hardness is also characterized by the ability to resist to both elastic and irreversible plastic deformations and can be affected signi ficantly by defects (i.e.,dislocations)and grain sizes [10].Therefore,hardness is not a quantity that can be easily determined in a well-de fined absolutescale [1].It has been often argued [13]that hardness measurements unavoidably suffer from an error of about 10%.All these aspects add huge complexity to a formal theoretical de finition of hardness [5e 9].Within this context,to find a simple way to estimate hardness of real materials is highly desirable.Unlike hardness,the elastic properties of materials can be measured and calculated in a highly accurate manner.Therefore,it has been historically natural to seek a correlation between hardness and elasticity.The early linear correlation between the hardness and bulk modulus (B)for several covalent crystals (diamond,Si,Ge,GaSb,InSb)was successfully established by Gilman and Cohen since 1950s [10,11].Nevertheless,successive studies demonstrated that an uniformed linear corre-lation between hardness and bulk modulus does not really hold for a wide variety of materials [1,12,13],as illustrated in Fig.1(a).Subsequently,Teter [12]established a better linear correlation between hardness and shear modulus (G ),as illustrated in Fig.1(b).This correlation suggests that the shear modulus,the resistance to reversible deformation under shear strain,can correctly provide an assessment of hardness for some materials.However,this correla-tion is not always successful,as discussed in Refs.[5,13,14].For instance,tungsten carbide (WC)has a very large bulk modulus (439GPa)and shear modulus (282GPa)but its hardness is only 30GPa [15],clearly violating the Teter ’s linear correlation [see*Corresponding authors.E-mail addresses:xingqiu.chen@ (X.-Q.Chen),dzli@ (D.Li).Contents lists available at ScienceDirectIntermetallicsjou rn al homepage:/locate/intermet0966-9795/$e see front matter Ó2011Elsevier Ltd.All rights reserved.doi:10.1016/j.intermet.2011.03.026Intermetallics 19(2011)1275e 1281Fig.1(b)][5].Although the link between hardness and elastic shear modulus can be arguable,it is certain to say that the Teter ’s correlation grasped the key.In this manuscript,following the spirit of Teter ’s empirical correlation,we successfully established a theoretical model on the hardness of materials through the introduction of the classic Pugh modulus ratio of G /B proposed in 1954[16].We found that the intrinsic correlation between hardness and elasticity of materials correctly predicts Vickers hardness for a wide variety of crystalline materials as well as BMGs.Our results suggest that,if a material is intrinsically brittle (such as BMGs that fail in the elastic regime),its Vickers hardness linearly correlates with the shear modulus (H v ¼0.151G ).This correlation also provides a robust theoretical evidence for the famous empirical correlation observed by Teter in 1998.On the other hand,our results demonstrate that the hardness of crystalline materials can be correlated with the product of the squared Pugh ’s modulus ratio and the shear modulus (H v ¼2ðk 2G Þ0:585À3where k is Pugh ’s modulus ratio).This formula provides the firm evidence that the hardness not only correlates with shear modulus as observed by Teter,but also with bulk modulus as observed by Gilman et al.Our work combines those aspects that were previously argued strongly,and,most importantly,is capable to correctly predict the hardness of all compounds included in Teter ’s [12],Gilman ’s [4,10],Gao et al.’s [5]and Sim u nek and Vacká r ’s [6]sets.Also,our model clearly demonstrates that the hardness of bulk metallic glasses is intrin-sically based on the same fundamental theory as the crystalline materials.We believe that our relation represents a step forward for the understanding and predictability of hardness.2.Model and resultsAccording to Vicker [10],the hardness of H v is the ratio between the load force applied to the indenter,F ,and the indentation surface area:H v ¼2F sin ðq =2Þd 2;(1)where d and q are the mean indentation diagonal and angle between opposite faces of the diamond squared pyramid indenter,respectively (Fig.2).In order to derive our model,we first assume that (i)the diamond squared pyramid indenter can be divided into four triangular based pyramid indenters and that (ii)the Vickers hardness is measured within the elastic scale.Then,for each triangular based pyramid,one can de fine the shear modulus G as,G ¼F4A tan ða Þ(2)which speci fies the ratio between shear stress and the shear strain.In terms of our model the exact shear area A on which the shear force (F )acts is unknown.But,the deformation area A *[A *¼1/8d 2tan(a )]delimited by the klO 0triangle is well de fined by the indentation geometry.Therefore,we can express the exact shear area (A )as:A ¼cA *¼c d 2tan ða Þ;(3)where c is the proportional coef ficient.It is clear that under elastic shear deformation the deformation area (A *)will be extremely small.However,upon real hardness measurements the deforma-tion area (A *)should be large enough so that the coef ficient c can be safely neglected and A z A *.Under this assumption,equation (2)can be revised as following,G ¼2F d 2tan 2ða Þ(4)Combining equations (1)and (4),the Vickers hardness readsH v ¼G tan 2ða Þsin ðq =2Þ¼0:92G tan 2ða Þ;(5)where the term sin(q /2)is intrinsically determined by the indenter itself,which can be considered as a constant (originated from the Vickers hardness,see equation (1)).For the diamond squared pyramid indenter with q ¼136 ,sin(q /2)is equal to 0.92for Vickers hardness measurement.In an ideal form of indentation,tan(a )¼0.404because of a ¼(p Àq )/2.0(c.f.,Fig.2).Therefore,equation (5)can be simpli fiedas,abFig.1.Correlation of experimental Vickers hardness (H v )with (a)bulk modulus (B )and with (b)shear modulus (G )for 39compounds (Table 2).Inset of panel (b):H v vs.G for 37BMGs (see Table 1).The solid line denotes empirical Teter ’s fitting values,whereas dashed lines correspond to the value derived from Eq.(6).The black and hollow squares denote data taken from Refs.[1,12].Fig.2.Illustration of indentation in terms of the squared diamond pyramid indenter.The red framework highlights one of four triangular based pyramid indenters.(For interpretation of the references to colour in this figure legend,the reader is referred to the web version of this article.)X.-Q.Chen et al./Intermetallics 19(2011)1275e 12811276H v¼0:151G:(6) Equation(6)represents a robust theoretical evidence of the linear correlation behavior observed by Teter[12],as reflected by the data shown in Fig.1(b).Residual discrepancies should be mainly attributed to the neglection of plastic deformation effects. Remarkably,we found that Eq.(6)is also valid for ing the experimental shear modulus G¼38.6(36.6)GPa[17]for Pd40Ni40P20BMG,the estimated Vickers hardness is5.83(5.53) GPa,in consistency with the experimental value of5.38GPa[17]. Similarly,for Fe41Co7Cr15Mo14C15B6Y2BMG by using the experi-mental G(84.3GPa[18])we obtained H v¼12.7GPa,in nice accord with the measured Vickers hardness of12.57Æ0.22GPa[18].As illustrated in the inset of Fig.1(b),the agreement with the experi-mental values is highly satisfactory for all37BMGs collected here (see Table1).Considering that BMGs are brittle materials without plastic fractures the data in the inset of Fig.1(b)strongly convey that our proposed formula(Eq.(6))is intrinsically connected to the shear modulus of materials if they fail in an elastic regime.It is highly difficult to realistically take plastic deformation into account in our modeling scheme.However,the indentation after a real hardness measurement shows the permanent plastic effect,which is,of course,reflected by the ratio l OO0=l OO00[namely, equal to tan(a)](see Fig.2).Note that the depth of the indenta-tion,l OO0,is parallel to the direction of shear deformation.We reasonably assume that its size should be closely correlated with the shear modulus of G,whereas the expansion wideness of the indentation,l OO00,is perpendicular to the direction of the loading force,hence,with almost little connection to the shear defor-mation.Therefore,the expansion wideness seems to reflect the ability to resist to compression effects,a property that should be related to bulk modulus,B.Accordingly,we proposed the following relation,tanðaÞf G=B:(7) Finally,combining equations(5)and(7),the hardness can be written as,H v f GðG=BÞ2:(8) It is interesting to note that in Eq.(8)the ratio of G/B is the famous modulus ratio proposed by Pugh in as early as1954[16].In his pioneer work,Pugh derived that the strain at fracture can be measured as3f(B/G)2.Indeed,hardness can be defined as the resistance to the applied stress at the critical strain of3that the system can sustain before yielding it to fracture.This clearly provides fundamental support for our model(Eq.(8)).Importantly, Pugh also highlighted a relation between the elastic and plastic properties of pure polycrystalline metals and stated that G/B is closely correlated to the brittle and ductility of materials[16]:the higher the value of G/B is,the more brittle the materials would be [16].Otherwise,the materials are expected to deform in a ductile manner with a low G/B value.This relation has been extensively accepted and applied not only to metal but also to high-strength materials[25e27].In principles,the covalent materials(such as diamond and c-BN)have the highest hardness but they are obvi-ously brittle with a larger Pugh modulus ratio.The strong covalent bonds indeed create a significant resistance to initialize the plastic flow to pin the dislocation,resulting in a quite high hardness. Conversely,ductile materials with a low Pugh’s modulus ratio are characterized by metallic bonding and low hardness.It is thus highly reasonable to establish a correlation between hardness and the modulus ratio G/B in Eq.(8).Thus,we revise further Eq.(8)in the following form,H v¼Ck m G n;ðk¼G=BÞ;(9)where H v,G and B are the hardness(GPa),shear modulus(GPa)and bulk modulus(GPa),respectively.The parameter k is the Pugh’s modulus ratio,namely,k¼G/B.C is a proportional coefficient.In order to derive the parameters C,m and n,wefirst selected ten materials with diamond-like(diamond,c-BN,b-SiC,Si and Ge), zinc-blende(ZrC and AlN)and rock-salt structures(GaP,InSb and AlSb)because their hardness,bulk and shear moduli are well-known(see Table2).By analyzing these data we found that C¼1.887,m¼1.171and n¼0.591.Hence,Eq.(9)becomes,H v¼1:887k1:171G0:591z1:887k2G0:585:(10) In order to assess the validity of this relation,we plot in Fig.3H v against k2G for a series of compounds.These data show a clear and systematic trend with k2G andfirmly establish a direct relation between hardness and k2G.Byfitting the data of Fig.3and revising further Eq.(10)we arrive to thefinal formula:Table1The comparison between the predicted Vickers hardness by Eq.(6)[H v¼0.151G]andthe experimental data for37bulk metallic glasses is shown,together with experi-mental Young modulus(E)and Shear modulus(G).The[XX];[YY]in thefirst columndenotes the reference numbers:XX is the reference for elastic constants and YY isthe reference for the Vickers hardness.Compounds E G H calc H expFe41Co7Cr15Mo14C15B6Y2[18]22684.312.7312.57Ni50Nb50[19]13248.27.268.93Ni40Cu5Ti17Zr28Al10[20]133.949.77.508.45Ni39.8Cu5.97Ti15.92Zr27.86Al9.95Si0.5[20]11743 6.498.13Ni40Cu5Ti16.5Zr28.5Al10[20]12245.2 6.837.84Ni45Ti20Zr25Al10[20]11442 6.347.76Ni40Cu6Ti16Zr28Al10[20]11140.9 6.187.65{Zr41Ti14Cu12.5Ni10Be22.5}98Y2[21]107.640.3 6.09 6.76Zr54Al15Ni10Cu19Y2[21]92.133.8 5.10 6.49Zr53Al14Ni10Cu19Y4[21]8631.5 4.76 6.44Zr41Ti14Cu12.5Ni8Be22.5C1[21]10639.5 5.96 6.13Zr46.75Ti8.25Cu7.5Ni10Be27.5[19]10037.2 5.62 6.1Zr48Nb8Cu14Ni12Be18[21]93.734.2 5.16 6.09Zr34Ti15Cu10Ni11Be28Y2[21]109.841 6.19 6.07Zr57Nb5Cu15.4Ni12.6Al10[19]87.331.9 4.82 5.9Zr48Nb8Cu12Fe8Be24[21]95.735.2 5.32 5.85Zr40Ti15Cu11Ni11Be21.5Y1Mg0.5[21]94.234.7 5.24 5.74Zr41Ti14Cu12.5Ni10Be22.5[19];[21]10137.4 5.65 5.97Zr41Ti14Cu12.5Ni10Be22.5[19];[22]10137.4 5.65 5.4Zr41Ti14Cu12.5Ni10Be22.5[19];[23]10137.4 5.65 5.88Zr41Ti14Cu12.5Ni10Be22.5[19]10137.4 5.65 5.23Zr65Al10Ni10Cu15[19]8330.3 4.58 5.6Zr65Al10Ni10Cu15[19]8331 4.58 5.6Zr57Ti5Cu20Ni8Al10[19]8230.1 4.55 5.4Cu60Hf10Zr20Ti10[19]10136.9 5.577Cu50Zr50[19]88.732.4 4.83 5.8Cu50Zr50[19]8532 4.83 5.8Cu50Zr45Al5[19]10233.3 5.03 5.4Pd40Ni40P20[19];[17]10838.6 5.83 5.38Pd40Ni40P20[17]e36.6 5.53 5.38Pd40Ni40P20[19]10838.6 5.83 5.3Pd40Ni10Cu30P20[19]9835.1 5.305Pd77.5Si16.5Cu6[19]92.932.9 5.25 4.5Pd77.5Si16.5Cu6[19]9634.8 5.25 4.5Pt60Ni15P25[19]9633.8 5.10 4.1Mg65Cu25Tb10[19]51.319.6 2.96 2.83Nb60Al10Fe20Co10[19]51.219.4 2.93 2.2Ce70Al10Ni10Cu10[19]3011.5 1.74 1.5Er55Al25Co20[24]70.7227.08 4.09 5.45Dy55Al25Co20[24]61.3623.52 3.55 4.7Tb55Al25Co20[24]59.5322.85 3.45 4.42Ho55Al25Co20[24]66.6425.42 3.84 4.14La55Al25Co20[24]40.915.42 2.33 3.48La55Al25Cu10Ni5Co5[24]41.915.6 2.363Pr55Al25Co20[24]45.917.35 2.62 2.58X.-Q.Chen et al./Intermetallics19(2011)1275e12811277H v ¼2 k 2G 0:585À3(11)from which we see that the hardness correlates not only with the shear modulus but also with the bulk modulus.Physically,the bulk modulus only measures isotropic resistance to volume change under hydrostatic strain,whereas shear modulus responses to resistance to anisotropic shear strain.Although the bulk modulus was thought to be less directly connected with hardness [5],the Pugh ’s modulus ratio k clearly contributes to the Vickers hardness.Equation (11)demonstrates that,if bulk modulus increases,hard-ness would decrease as long as the shear modulus remains unchanged,and vice versa.This behavior can be understood by the fact that if the Pugh ’s modulus ratio G /B gets smaller with increasing bulk modulus,the material would become more ductile.Its hardness can be thus expected to have a lower value.Taking the example,the compounds TiN and b -SiC have almost the same experimental shear moduli [28,29]of 187.2GPa and 191.4GPa,Table 2Comparison between theoretical values within our current model and experimental values as compared with available theoretical findings obtained through the modelsof Gao et al.[5]and of Sim u nek and Vack a r [6].Furthermore,the bulk and shearmoduli are compiled in this table.In the last column,“e ”and “c ”denote elastic data (G and B )from direct experimental and theoretical calculations,respectively.The Pugh ’s modulus ratio k is compiled in this table.For details,see text.The [XX];[YY]in the first column denotes the reference numbers:XX is the reference for elastic constants and YY is the reference for the Vickers hardness.Note that,as the experimentally measured hardness is a highly complex property,which depends on the loading force and the quality of samples.For instance,the hardness of diamond was reported in the range from 60GPa to 120GPa.For the sake of convenientcomparisons with the known Gao et al.[5]and Sim u nek and Vack a r ’s [6]models,here we also quoted the same values as previous models quoted [5,6,7,8,9].If compounds were not discussed in the previous theoretical models [5,6,7,8],here the experimental Vicker ’s hardness was selected,mainly based on the saturated Vickers hardness value (namely load-invariant indentation hardness)at a relatively large loading force.For instance,for ReB 2,we selected the Vicker ’s hardness of 30.1GPa at the large loading forces of 4.9N as discussed in Ref.[46].Compounds G B k H calc H exp H Gao H SimunekDiamond [28];[5]535.5442.3 1.21195.79693.695.4e Diamond [28];[5]548.3465.5 1.17893.99693.695.4c Diamond [35];[5]520.3431.9 1.20593.59693.695.4c Diamond [12];[5]535.0443.0 1.20895.49693.695.4e BC 2N [36];[37]446.0403.0 1.10776.9767871.9c BC 2N [12];[37]445.0408.0 1.09175.4767871.9e BC 5[38];[32]394.0376.0 1.04866.771c c-BN [39];[5]405.4400.0 1.01465.26664.563.2e c-BN [28];[5]403.4403.70.99963.86664.563.2c c-BN [28];[5]382.2375.7 1.01763.16664.563.2c c-BN [40];[5]404.4384.0 1.05368.26664.563.2c c-BN [12];[5]409.0400.0 1.02366.26664.563.2e g -B 28[41];[33]236.0224.0 1.05449.050c B 60[12];[42]204.0228.00.89536.438e b -SiC [28];[5]191.4224.70.85232.83430.331.1e b -SiC [28];[5]196.6224.90.87434.53430.331.1c b -SiC [43];[5]190.2209.20.90935.53430.331.1c b -SiC [44];[5]186.5220.30.84632.03430.331.1e b -SiC [12];[5]196.0226.00.86734.13430.331.1e SiO 2[12];[12]220.0305.00.72129.03330.4e ReB 2[45];[46]273.0382.00.71532.930.1e WC [47];[5]301.8438.90.68833.43026.421.5e WC [5]282.0439.00.64229.33026.421.5e B 4C [48];[12]192.0226.00.85032.830a e VC [This work];[6]209.1305.50.68526.22927.2c ZrC [49];[30]169.7223.10.76126.325.8e ZrC [50];[30]182.5228.30.79929.425.8c ZrC [50];[30]185.9228.00.81530.525.8c ZrC [51];[30]169.6223.30.75926.225.8e ZrC [12];[30]166.0223.00.74425.225.8e TiC [49];[6]182.2242.00.75327.124.718.8e TiC [52];[6]176.9250.30.70724.524.718.8c TiC [15];[6]198.3286.00.69325.824.718.8c TiC [53];[6]187.8241.70.77728.824.718.8e TiC [12];[6]188.0241.00.78029.024.718.8e TiN [54];[30]183.2282.00.65022.52318.7c TiN [29];[30]187.2318.30.58820.02318.7e TiN [55];[30]205.8294.60.69926.72318.7c TiN [56];[30]207.9326.30.63723.82318.7c RuO 2[57];[5]142.2251.30.56615.72020.6c RuO 2[58];[5]173.0248.00.69823.72020.6c AlO 2[40];[5]161.0240.00.67121.52020.6c AlO 2[40];[5]160.0259.00.61819.22020.6c AlO 2[59];[5]164.0254.00.64620.72020.6e AlO 2[12];[5]162.0246.00.65921.12020.6e NbC [60];[6]171.0333.00.51315.51818.3c NbC [51];[6]171.7340.00.50515.21818.3e AlN [40];[5]134.7206.00.65418.41821.717.6c AlN [61];[5]130.2212.10.61416.51821.717.6c AlN [62];[5]123.3207.50.59415.21821.717.6c AlN [63];[5]132.0211.10.625171821.717.6e AlN [12];[5]128.0203.00.63116.91821.717.6e NbN [64];[6]155.9292.00.53415.41719.5e NbN [12];[6]156.0315.00.49513.91719.5e HfN [28];[30]186.3315.50.59120.017c HfN [28];[30]164.8278.70.59118.417c GaN [65];[5]105.2175.90.59813.715.118.118.5e GaN [12];[5]120.0210.00.57114.115.118.118.5eTable 2(continued )Compounds G B k H calc H exp H Gao H SimunekZrO 2[40];[5]88.0187.00.4718.41310.8c ZrO 2[66];[5]93.0187.00.4979.51310.8e Si [49];[5]66.697.90.68011.81213.611.3e Si [67];[5]64.097.90.65410.91213.611.3c Si [67];[5]63.290.70.69711.81213.611.3c Si [68];[5]61.796.30.64010.21213.611.3c Si [68];[5]61.789.00.69311.51213.611.3c GaP [51];[5]55.788.20.6319.39.58.98.7e GaP [49];[5]55.888.80.6289.29.58.98.7e GaP [47];[5]56.188.60.6339.49.58.98.7e GaP [70];[5]61.989.70.69011.59.58.98.7c AlP [71];[5]49.086.00.5707.19.49.67.9e AlP [70];[5]51.890.00.5757.59.49.67.9c AlP [72];[5]48.886.00.5677.09.49.67.9c InN [65];[6]55.0123.90.444 5.1910.48.2c InN [71];[6]77.0139.60.5529.7910.48.2c Ge [67];[6]53.172.20.73611.38.811.79.7c Ge [67];[6]43.860.30.7269.58.811.79.7c GaAs [47];[5]46.575.00.6207.87.58.07.4e GaAs [69];[5]46.775.50.6197.87.58.07.4e GaAs [49];[5]46.775.40.6197.87.58.07.4e YO 2[28];[5]72.5166.00.437 6.37.57.7c YO 2[28];[5]62.7146.50.428 5.37.57.7c YO 2[73];[5]66.5149.30.445 6.07.57.7e InP [69];[5]34.371.10.483 3.8 5.4 6.0 5.1e InP [47];[5]34.472.50.475 3.6 5.4 6.0 5.1e AlAs [74];[5]44.877.90.575 6.758.5 6.8e AlAs [69];[5]44.678.30.569 6.558.5 6.8e GaSb [69];[6]34.256.30.607 5.8 4.5 6.0 5.6e GaSb [49];[6]34.156.40.606 5.8 4.5 6.0 5.6e GaSb [47];[6]34.356.30.608 5.8 4.5 6.0 5.6e AlSb [75];[5]31.556.10.561 4.64 4.9 4.9c AlSb [69];[5]31.958.20.549 4.54 4.9 4.9e AlSb [49];[5]32.559.30.548 4.64 4.9 4.9e AlSb [47];[5]31.958.20.549 4.54 4.9 4.9e InAs [69];[5]29.557.90.509 3.6 3.8 5.7 4.5e InAs [49];[5]29.559.10.499 3.4 3.8 5.7 4.5e InSb [47];[5]23.046.90.490 2.4 2.2 4.3 3.6e InSb [69];[5]22.946.50.492 2.4 2.2 4.3 3.6e InSb [49];[5]22.946.00.498 2.5 2.2 4.33.6e ZnS [49];[6]32.878.40.418 2.5 1.8 2.7e ZnS [47];[6]31.577.10.408 2.3 1.8 2.7e ZnSe [47];[6]28.863.10.456 2.7 1.4 2.6e ZnTe [47];[6]23.451.00.459 2.11 2.3e ZnTe [49];[6]23.451.00.4592.112.3eaB 4C was suggested to be very hard in Ref.[76].Mukhanov et al.recently pre-dicted that the Vickers hardness of B 4C was 45.0GPa [85]in agreement with the reported experimental data of 45GPa in Ref.[77](see Table 1in Ref.[85]).However,we also noted that Teter [12]ever summarized the hardness of B 4C with a value of 30Æ2GPa.In addition,the experimental value of 32e 35GPa was recently summarized in the handbook [78].Therefore,here we quoted the experimental Vickers hardness of 30GPa,as summarized by Teter in Ref.[12].X.-Q.Chen et al./Intermetallics 19(2011)1275e 12811278respectively.However,the experimental bulk modulus of TiN (318.3GPa)[28,29]is larger by about 42%than that of b -SiC (224.7GPa).In terms of our formula,b -SiC is found to be harder than TiN,in agreement with the experimental observations [5,30][Expt (Calc in this work):TiN with H v ¼23(20)GPa and b -SiC with H v ¼34(33)GPa].To further assess the performance of our model (Eq.(11))we show in Fig.4a comparison between the estimated and experi-mental values for a series of compounds (see Table 2),con firming a good agreement.Also WC,which is wrongly found to be a superhard (49GPa)material within Teter ’s linear correlation,is now predicted to have a Vickers hardness of 29.3GPa in very good accordance with experimental value (30GPa [15]).In particular,Figs.3and 4convey that our proposed formula reproduced very well the Vickers hardness for all well-known superhard materials (Diamond [5,6],BC 2N [5,6,12,31],c-BN [5,6],c-BC 5[32],and g -B 28[33,34]).The interesting case is the compound of ReB 2,which was thought to be superhard [79].Although its Vickers hardness was debated extensively [79e 81],there is now a wide-accepted consensus that its Vickers hardness of 30.1GPa at the large loading force of 4.9N [45,46].Using the experimentally measured bulk and shear moduli [45](B ¼273GPa and G ¼382GPa)and interms of our Eq.(11),the Vickers hardness is derived to be 32.9GPa,in nice agreement with the experimental data [45,46].In addition,we also noted that Qin et al.claimed that the Vickers ’hardness of ReB 2is only about 18GPa for a densi fied compact sample [82].However,this value was very recently demonstrated experimen-tally not to re flect well the intrinsic hardness of ReB 2[83]because that the amorphous boron,which is not detectable only by powder X-ray diffraction (Fig.2in Ref.[82]),exists in the synthetic samples in the Qin et al.’s experiment [82].As reported in Ref.[83],the ReB 2sample synthesized by spark plasma sintering exhibits a Vickers hardness of 27.0Æ 4.7GPa.Through a tri-arc crystal-growing furnace the synthesized high-purity ReB 2crystal with grains oriented with respect to the c-axis has a high hardness value of 39.5Æ2.5GPa [83].Again,our estimated Vickers hardness value is still within these experimental values.Another attention has to be paid to the case of B ing the experimental bulk and shear moduli (B ¼230GPa and G ¼206GPa)[84],its Vickers hardness is calculated to be 36.7GPa within our current model.This value is well within the scale of the experimentally measured results from 32to 38GPa [42,84]for polycrystalline boron suboxide sintered samples,although a Vick-ers hardness of 45GPa was reported for the single crystals under a loading force of 0.98N [42].Indeed,the light loading force of 0.98N is not large enough to obtain a real hardness.It is thus ex-pected to have a smaller hardness if a loading force larger than 0.98N is applied.Our estimated value for B 6O is also in good agreement with the derived value of 37.3GPa through a very recent thermodynamic model of hardness [83].We further estimated two more phases of carbon (C 4and M -carbon),which were suggested to be superhard [86e 90].Utilizing elastic shear and bulk moduli obtained in Ref.[88],the Vickers hardness of C 4is calculated to be 69.0GPa (c.f.,Fig.3)that is comparable to the superhard c-BN.Moreover,through using the calculated bulk and shear moduli (B ¼415GPa and G ¼468GPa [91])for the M -carbon phase,we obtained its Vickers hardness of 81.0GPa (c.f.,Fig.3),placing M-carbon in between BC 2N and dia-mond,agreeing well with the value (83.1GPa)obtained by Simunek ’s model [87].In addition,from Fig.4all estimated data are in good agreement with those obtained from pervious models [5,6].Nevertheless,we would like to emphasize that,although our proposed model canreproduce well the results obtained by Gao et al.’s [5]and Sim u nekand Vack a r [6]models,the underlying mechanism is substantiallydifferent.Gao ’s and Sim u nek and Vack a r ’s models are based onbond properties such as bond length,charge density,ionicity and their strengths and coordinations in crystalline materials.Differ-ently,our model depends totally on the so-called polycrystalline moduli (bulk and shear moduli as well as Pugh ’s modulus ratio),which indeed response directly to the abilities of resistance under loading forces for polycrystalline materials.As demonstrated above,for polycrystalline materials the introduced Pugh ’s modulus ratio in our model plays a crucial role in elucidating plastic deformation,which is intrinsically different from all known semi-empirical hardness models [5e 9].3.Discussion and remarksThe hardness of a material is the intrinsic resistance to defor-mation when a force is applied [1].Currently,a formal theoretical de finition of hardness is still a challenge for materials scientists.The need for alternative superhard and ultrahard materials for modern technology has brought a surge of interest on modeling and predicting the hardness of real materials.In particular,in recent years several different semi-empirical models for hardness of polycrystalline covalent and ionic materials have beenproposed.Fig.4.Correlation between experimental and theoretical Vickers hardness (H v )for 39compounds,as compared with the estimated data from the models [5,6](see Table 2).Fig.3.Experimental Vickers hardness as a function of the product k 2G (k ¼G /B ).All data are collected from literature (see Table 2).X.-Q.Chen et al./Intermetallics 19(2011)1275e 12811279。

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