网络行为学研究综述

详细介绍了网络行为学的概念,并将网络行为分为微观行为和宏观行为两个类别,最后论述了网络测量在网络行为学研究中的重要地位和网络行为的研究方向。

P2P网络行为学研究

对等网络结构不断发展,网络中节点的行为逐渐成为影响网络效率的重要因素。激励、信誉和公平性已经成为对等网络中的三大难题,然而这三者都属于网络行为学的范畴。针对P2P网络行为学中的难题,提出了解决方法即在设计P2P系统的同时要兼顾激励、信誉和公平性。并对这三者分别进行了分析比较,针对当今不同P2P结构中的节点行为进行了评价。最后进一步展望了P2P网络行为学的研究方向。

网络测量及行为学研究综述

随着互联网的发展,理解网络行为对于网络管理、规划和发展都有重要意义,网络流量测量是研究网络行为的基础。论文首先根据目前Internet发展的特点、网络管理的缺点和不足,分析网络行为学的研究难点和重点。将网络测量及行为学研究分成测量、流量行为分析、端至端行为分析等三个方面,并分别从这三个方面的国内外研究状况进行分析总结。论文最后定义网络行为学的概念,并提出网络行为学的研究方向。

“网络测量与行为学”课程教学探讨

互联网的发展使得网络测量和行为分析成为监控、理解、认识网络进而实现对网络精细化管理的重要方法。我院于2009年开始为研究生开设了"网络测量与行为学"课程。本文探讨了该课程设置的必要性和教学方法,并给出该课程所涉及的相关资料,以期吸引国内高校能够开设该课程,进而培养更多的网络测量与行为学相关人才。

网络测量及其关键技术

网络测量对网络研究与发展十分重要.分析了网络测量的常用方法,论述了网络测量的体系结构和研究现状.同时还指出了端到端时延模型、"噪声"分组过滤、测试点最优选择、业务模型分析和时钟偏移影响的消除等网络测量的关键技术.最后说明网络测量在研究网络行为学、网络管理、网络安全和研究网络的新方法、新技术等方面的应用.

基于异构系统的统一网络安全监控体系模型

针对大规模异构网络环境下安全行为的复杂性,现有的网络技术与管理缺少对海量原始数据的良好安全监控手段,本文提出构建统一的网络安全监控体系的思想,对各种异构数据源数据(审计日志和流量数据)进行标准化表示和整合,采用数据挖掘和小波分析的方法对数据进行分析处理,通过关联规则、流量规则和规则序列模式分析出整个网络的运行情况,对系统分析结果给出可视化结论,调整安全策略以适应网络安全动态性和整体性.

泊松采样在基于RMON的网络测量中的研究及实现

网络流量测量和分析是网络行为学研究的基础,文章通过对目前网络测量的技术及采样技术的分析研究,应用基于RMON的网络流量测量和泊松采样的方法,对RMON代理中探测到的网络流量数据进行采样、分析。文章对认识网络流量的一般规律,掌握网络行为的基本特征具有一定的参考价值。

TCP数据流的非对称性分析

提出了一个面向TCP流属性的非对称性测度,包括该测度的定义和计算方法。并基于这个测度对CERNET的一个千兆省网边界的实际数据进行了分析。分析工作从流长度(报文数)和流字节数2个属性角度展开,利用一组函数对这些流属性的非对称测度进行了统计和分析,获得了一组有关的结论,可以用于与网络行为学有关的研究。

Different Epidemic Models on Complex Networks

Models for diseases spreading are not just limited to SIS or SIR. For instance, for the spreading of AIDS/HIV, the susceptible individuals can be classified into different cases according to their immunity, and similarly, the infected individuals can be sorted into different classes according to their infectivity. Moreover, some diseases may develop through several stages. Many authors have shown that the individuals＇ relation can be viewed as a complex network. So in this paper, in order to better explain the dynamical behavior of epidemics, we consider different epidemic models on complex networks, and obtain the epidemic threshold for each ease. Finally, we present numerical simulations for each case to verify our results.

Approach to Anomaly Traffic Detection in a Local Network

The research intends to solve the problem of the occupation of bandwidth of local network by abnormal traffic which affects normal user's network behaviors.Firstly,a new algorithm in this paper named danger-theory-based abnormal traffic detection was presented.Then an advanced ID3 algorithm was presented to classify the abnormal traffic.Finally a new model of anomaly traffic detection was built upon the two algorithms above and the detection results were integrated with firewall.The firewall limits the bandwidth based on different types of abnormal traffic.Experiments show the outstanding performance of the proposed approach in real-time property,high detection rate,and unsupervised learning.

Research on College Frustrated Students＇ Network Mentality and Behaviors

Network has not only become a habit and lifestyle for university student, but also brought all sorts of ethical misconducts and ethical issues in society. Based on the analysis of college students＇ frustrations, this paper explores the causes of network behavior anomie for college students, which mainly include： dissatisfaction in real communication, game addiction to the network, craving online pornography, and hooking on online shopping. In addition, it also investigates the ways to wipe out mental frustration in such a cyber era. These ways mainly are to strenzthen online education and management, to make psychological counseling, and to carry on frustration education.

An Empirical Research on the Behavior and Influencing Factors of College Students Network Course Learning

＂Using technology to support learning＂ gradually becomes the main trend of technology application and technology-curriculum integration at home and abroad. The empirical research based on newly-established universities shows that college students＇ usage intention on network course and study behavior is not real. Study behavior of network course is passive and outside stimulation. They are more concerned about the usefulness and relative advantages of the learning mode in the network course learning; however, learning environment of the network course at present still is given on material and tool level. The institutions should, in compliance with the trend of higher education development in the world, actively build learning environment to meet the learning needs of students in the information age, deepen the reform of information technology application and curriculum based on the learner-centered concept, focus on the training of initiative learning of college students, put community impact into full play to promote the integration of the superiority of the material platform and initiative, and finally improve the efficiency and quality of teaching and education.

Bistability Analysis of Excitatory-Inhibitory Neural Networks in Limited-Sustained-Activity Regime

Bistable behavior of neuronal complex networks is investigated in the limited-sustained-activity regime when the network is composed of excitatory and inhibitory neurons.The standard stability analysis is performed on the two metastable states separately.Both theoretical analysis and numerical simulations show consistently that the difference between time scales of excitatory and inhibitory populations can influence the dynamical behaviors of the neuronal networks dramatically,leading to the transition from bistable behaviors with memory effects to the collapse of bistable behaviors.These results may suggest one possible neuronal information processing by only tuning time scales.

Sleeping of a Complex Brain Networks with Hierarchical Organization

The dynamical behavior in the cortical brain network of macaque is studied by modeling each cortical area with a subnetwork of interacting excitable neurons. We characterize the system by studying how to perform the transition, which is now topology-dependent, from the active state to that with no activity. This could be a naive model for the wakening and sleeping of a brain-like system, i.e., a multi-component system with two different dynamical behavior.

An Analysis of the Cognitive Constraints of Parody in the Context of Network Media

The language used in the context of network media features variation, flexibility, and comprehensiveness, in which parody is one of the most employed devices. Modern cognitive science prefers to take parody as a cognitive act rather than a sheer linguistic modification act, which has provided a new prospective for further research about parody in the context of network media. This paper aims to probe into the cognitive constraints in the mechanism of parody in the context of network media, in hope to make a detailed explanation of the cognitive process of it and to strengthen the comprehension and appreciation of it.

Evaluation on the Thermal Stability and Hazards Behaviors of ADVN Using Green Thermal Analysis Approach

ADVN （2,2＇-Azobis （2,4-dimethyl） valeronitrile）, a free radical initiator, is widely applied for the polymerization reaction of polymers in the chemical industries. When ADVN releases free radical during the decomposition process, it can accompany abundant heat and huge pressure to increase the possibility of thermal runaway and hazard, causing unacceptable thermal explosion or fire accidents. To develop an inherently safer process for ADVN, the thermal stability parameters of ADVN were obtained to investigate thermal decomposition characteristics using a DSC （differential scanning calorimetry） and TG （thermogravimetry）. We used various kinetic models to completely depict the kinetic behavior and determine the thermal safety parameters for ADVN. The green thermal analysis approach could be used to substitute for complicated procedures and large-scale experiments of traditional thermal analysis methods, avoiding environmental pollution and energy depletion.

Tsallis Mapping in Growing Complex Networks with Fitness

We introduce Tsallis mapping in Bianconi-Barabgsi （B-B） fitness model of growing networks. This mapping addresses the dynamical behavior of the fitness model within the framework of nonextensive statistics mechanics, which is characterized by a dimensionless nonextensivity parameter q. It is found that this new phenomenological parameter plays an important role in the evolution of networks： the underlying evolving networks may undergo a different phases depending on the q exponents, comparing to the original B-B fitness model, and the corresponding critical transition temperature Tc is also identified.

Degree Distribution Dynamics for Disease Spreading with Individual Awareness

Behavioral responses triggered by the perceived risk of experiencing the disease represent a key ingredient in the spread of epidemics across human population.In this paper,two forms of individual awareness(i.e.,the risk perception of an emerging epidemic) are addressed:Contact awareness that increases with individual contact number,and local awareness that increases with the fraction of infected contacts.By extending the probability generating functionology,the author shows that it is possible to track the evolution of the degree distributions among susceptible and infected individuals when the underlying network of contacts is represented by a semi-random configuration model.It is hopefully to shed some light on the dynamic aspects of networked epidemiological models.

Learning embeddings of a heterogeneous behavior network for potential behavior prediction

Potential behavior prediction involves understanding the latent human behavior of specific groups,and can assist organizations in making strategic decisions.Progress in information technology has made it possible to acquire more and more data about human behavior.In this paper,we examine behavior data obtained in realworld scenarios as an information network composed of two types of objects(humans and actions)associated with various attributes and three types of relationships(human-human,human-action,and action-action),which we call the heterogeneous behavior network(HBN).To exploit the abundance and heterogeneity of the HBN,we propose a novel network embedding method,human-action-attribute-aware heterogeneous network embedding(a4 HNE),which jointly considers structural proximity,attribute resemblance,and heterogeneity fusion.Experiments on two real-world datasets show that this approach outperforms other similar methods on various heterogeneous information network mining tasks for potential behavior prediction.

Finite-Time Generalized Outer Synchronization Between Two Different Complex Networks

This paper investigates the finite-time generalized outer synchronization between two complex dynamical networks with different dynamical behaviors. The two networks can be undirected or directed, and they may also contain isolated nodes and clusters. By using suitable controllers, sufficient conditions for finite-time generalized outer synchronization are derived based on the finite-time stability theory. Finally, numerical examples are examined to illustrate the effectiveness of the analytical results. The effect of control parameters on the synchronization time is also numerically demonstrated.