Network User Behavior Research Articles

Characterization of Wireless Multidevice Users

The number of wireless-enabled devices owned by a user has had huge growth over the past few years. Over one third of adults in the United States currently own three wireless devices: a smartphone, laptop, and tablet. This article provides a study of the network usage behavior of today’s multidevice users. Using data collected from a large university campus, we provide a detailed multidevice user (MDU) measurement study of more than 30,000 users. The major objective of this work is to study how the presence of multiple wireless devices affects the network usage behavior of users. Specifically, we characterize the usage pattern of the different device types in terms of total and intermittent usage, how the usage of different devices overlap over time, and uncarried device usage statistics. We also study user preferences of accessing sensitive content and device-specific factors that govern the choice of WiFi encryption type. The study reveals several interesting findings about MDUs. We see how the use of tablets and laptops are interchangeable and how the overall multidevice usage is additive instead of being shared among the devices. We also observe how current DHCP configurations are oblivious to multiple devices, which results in inefficient utilization of available IP address space. All findings about multidevice usage patterns have the potential to be utilized by different entities, such as app developers, network providers, security researchers, and analytics and advertisement systems, to provide more intelligent and informed services to users who have at least two devices among a smartphone, tablet, and laptop.

Recommendation information diffusion in social networks considering user influence and semantics

One of the major problems in the domain of social networks is the handling and diffusion of the vast, dynamic and disparate information created by its users. In this context, the information contributed by users can be exploited to generate recommendations for other users. Relevant recommender systems take into account static data from users’ profiles, such as location, age or gender, complemented with dynamic aspects stemming from the user behavior and/or social network state such as user preferences, items’ general acceptance and influence from social friends. In this paper, we enhance recommendation algorithms used in social networks by taking into account qualitative aspects of the recommended items, such as price and reliability, the influencing factors between social network users, the social network user behavior regarding their purchases in different item categories and the semantic categorization of the products to be recommended. The inclusion of these aspects leads to more accurate recommendations and diffusion of better user-targeted information. This allows for better exploitation of the limited recommendation space, and therefore, online advertisement efficiency is raised.

Design and analysis of the covert channel implemented by behaviors of network users

AbstractIn this paper, a novel covert channel, called covert behavior channel, is proposed according to behaviors of network users to solve the security and efficiency problem of the traditional covert channel. In the proposed channel, operation sequences of the network protocols are used as carriers of covert information. An encryption‐based information embedding scheme is designed to improve security of the covert information. With the help of Markov model, the capacity of the proposed covert channel with time‐varying noise is derived. The formulation for analyzing the covert behavior channel is presented against the channel noise aroused by discarding packets. By introducing corrected entropy‐based algorithm to detect the covert behavior channel, the security of the channel is verified. Numerical results show that the proposed covert behavior channel is more secure than covert storage channels and achieves a better bit rate and robustness than that of covert timing channels. Copyright © 2016 John Wiley & Sons, Ltd.

Towards Increasing Feedbacks and Diffusion of Information in Social Networks

The purpose of this document is to highlight two strategies which increased feedbacks and stimulated the diffusion of information in social networks. Starting by an online survey, which assists searchers in understanding the behavior of social network users, to define the problem. The information received with this survey has been tested on validation by an analyze on the most popular type of posting on social networks. The results of this study provided two strategies as a solution for the problem of dissatisfaction among users of social networks and to increase feedbacks and diffusion of information on social network. One strategy is manual and is based on the type of publication and the second strategy is automatically and takes the concept of collaborative work in to account

Research and Application of Network User Behavior Data Mining Under the Background of Big Data

Research and Application of Network User Behavior Data Mining Under the Background of Big Data

A Coevolution Model of Network Structure and User Behavior: The Case of Content Generation in Online Social Networks

With the rapid growth of online social network sites (SNSs), it has become imperative for platform owners and online marketers to quantify what factors drive content production on these platforms. Previous research identified challenges in modeling these factors statistically using observational data, where the key difficulty is the inability of conventional methods to disentangle the effects of network formation and network influence on content generation from the subsequent feedback effect of newly generated content on network structure. In this paper, we adopt and enhance an actor-oriented continuous-time statistical model that enables the joint estimation of the coevolution of the users’ social network structure and of the amount of content they produce, using a Markov chain Monte Carlo–based simulation approach. Specifically, we offer a method to analyze nonstationary and continuous-time behavioral data, typically recorded in social media ecosystems, in the presence of network effects and other observable and unobservable user-specific covariates. The proposed method can help disentangle network effects of interest from feedback effects on the network. We apply our model to social network and public posting data over six months to find that (1) users tend to connect with others that have similar posting behavior; (2) however, after doing so, these users tend to diverge in their posting behavior, and (3) peer influence effects are sensitive to the strength of the posting behavior. More broadly, the proposed method provides researchers and practitioners with a statistically rigorous approach to analyze network effects in observational data. Our results lead to insights and recommendations for SNS platform owners on how to sustain an active and viable community.The online appendix is available at https://doi.org/10.1287/isre.2018.0790 .

Clustering and Profiling IP Hosts Based on Traffic Behavior

The objective of this research is to study the behavior of IP Network nodes (IP hosts) from the prospective of their communication behavior patterns to setup hosts’ behavior profiles of the observed IP nodes by clustering hosts into clusters of similar communication behaviors. The problem of IP address behavior analysis and profile establishment is the one that not fully discussed and the results achieved are not good enough, there is no complete solution yet. There are many potential applications of this work, the results of this research will be useful to the network management and Network security situation awareness in addition to the applications in studying the network user behavior. The contribution of this paper includes: 1) discussion about the features or host behavior communication patterns to be utilized in hosts clustering to characterize accurately and efficiently groups of host behavior traffic. 2) We presented an algorithm to extract most significant IP nodes to be analyzed instead of analyzing the complete list of millions of IP nodes that exist in the trace. 3) We analyzed IP nodes traffic behavior on relatively long periods of traces, which help to extract a more stable host’s behavior. While previous studies focus only on host behavior for relatively short periods of 5 to 15 minutes, we extract host’s behavior patterns over a period of one hour which needs big data analysis to provide results in a reasonable time.

ML-SOR: Message routing using multi-layer social networks in opportunistic communications

Opportunistic networks are a generalization of DTNs in which disconnections are frequent and encounter patterns between mobile devices are unpredictable. In such scenarios, message routing is a fundamental issue. Social-based routing protocols usually exploit the social information extracted from the history of encounters between mobile devices to find an appropriate message relay. Protocols based on encounter history, however, take time to build up a knowledge database from which to take routing decisions. While contact information changes constantly and it takes time to identify strong social ties, other types of ties remain rather stable and could be exploited to augment available partial contact information. In this paper, we start defining a multi-layer social network model combining the social network detected through encounters with other social networks and investigate the relationship between these social network layers in terms of node centrality, community structure, tie strength and link prediction. The purpose of this analysis is to better understand user behavior in a multi-layered complex network combining online and offline social relationships. Then, we propose a novel opportunistic routing approach ML-SOR (Multi-layer Social Network based Routing) which extracts social network information from such a model to perform routing decisions. To select an effective forwarding node, ML-SOR measures the forwarding capability of a node when compared to an encountered node in terms of node centrality, tie strength and link prediction. Trace driven simulations show that a routing metric combining social information extracted from multiple social network layers allows users to achieve good routing performance with low overhead cost.

Performance Evaluation and Measurements of 3G Mobile Communication Networks: A Case Study

Communication networks have undergone rapid developments in the past few decades in many Sub-Saharan African countries. The increasing number of subscribers and demand for greater variety of services in these countries make it difficult for network operators to provide the service varieties subscribers want while maintaining acceptable levels of quality of service. This paper analyzes the radio network of cellular networks in terms of traffic distribution over the existing number of communication channels using MATLAB/Simulink. A scale-free user network, which takes into account user behavior in a realistic physical network, has been used to model a more realistic cause for call blockings in a typical cellular network deployment under a Sub-Saharan environment. Peak recorded traffic distribution was observed to have overwhelmed the existing number of channels provided by the network operators for some cells eventually leading to increase in call drop rates. This high call blocking probability was attributed to poor network monitoring by the network operators to match the ever changing traffic intensities.

Social Networks in which Users are not Small Circles

Understanding of social network structure and user behavior has important implications for site design, applications (e.g., ad placement policies), accurate modeling for social studies, and design of next-generation infrastructure and content distribution systems. Currently, characterizations of social networks have been dominated by topological studies in which graph representations are analyzed in terms of connectivity using techniques such as degree distribution, diameter, average degree, clustering coefficient, average path length, and cycles. The problem is that these parameters are not completely satisfactory in the sense that they cannot account for individual events and have only limited use, since one can produce a set of synthetic graphs that have the exact same metrics or statistics but exhibit fundamentally different connectivity structures. In such an approach, a node drawn as a small circle represents an individual. A small circle reflects a black box model in which the interior of the node is blocked from view. This paper focuses on the node level by considering the structural interiority of a node to provide a more fine-grained understanding of social networks. Node interiors are modeled by use of six generic stages: creation, release, transfer, arrival, acceptance, and processing of the artifacts that flow among and within nodes. The resulting description portrays nodes as comprising mostly creators (e.g., of data), receivers/senders (e.g., bus boys), and processors (re-formatters). Two sample online social networks are analyzed according to these features of nodes. This examination points to the viability of the representational method for characterization of social networks.

ソーシャル機能を持つビデオ・オン・デマンドサービスにおけるユーザの視聴・コミュケーション行動間の関係分析

The spread of Video on Demand (VOD) services is driving the creation of video viewing environments in which users can watch whatever they like, whenever they like. The recent appearance of social networking services (SNSs), moreover, is bringing big changes to the world of media by enabling anyone to become a disseminator of information. We are studying a platform that combines VOD and SNS to create ``horizontal links'' between program viewers, and to facilitate encounters with new programs. To investigate user behaviors on this platform, we built an SNS site called ``teleda,'' that enables program viewing by VOD, and conducted a large-scale, three-month verification trial with about 1000 participants. In this paper, we report on the relational analysis of teleda users' viewing and communication behaviors. In order to clarify how the users' communication structures relate to the viewing and posting behaviors on this system, we described the communication structures in terms of network structures, and ran a correlation analysis of network indicators and user behavior indicators such as viewing and posting. As a result, we revealed that relationships between the users' communication structures, viewing behaviors, and posting actions are characteristics of each program's genre.

Animal transportation networks.

Many group-living animals construct transportation networks of trails, galleries and burrows by modifying the environment to facilitate faster, safer or more efficient movement. Animal transportation networks can have direct influences on the fitness of individuals, whereas the shape and structure of transportation networks can influence community dynamics by facilitating contacts between different individuals and species. In this review, we discuss three key areas in the study of animal transportation networks: the topological properties of networks, network morphogenesis and growth, and the behaviour of network users. We present a brief primer on elements of network theory, and then discuss the different ways in which animal groups deal with the fundamental trade-off between the competing network properties of travel efficiency, robustness and infrastructure cost. We consider how the behaviour of network users can impact network efficiency, and call for studies that integrate both network topology and user behaviour. We finish with a prospectus for future research.

Design and Implementation of Service Traffic Awareness System in LTE Network

Based on telecom operators increasingly urgent demand for intelligent pipeline, this paper has proposed a kind of implementing scheme of traffic recognition system in LTE network. Aiming at the traditional monitoring system with poor user perception and insufficient statistical capacity, we achieved a self - learning refining identification system of application layer services, and further analyzed the characteristic of user behavior in mobile network which relying on the traditional monitoring system and applying current prevailing Internet behavior analysis technologies-deep packet inspection (DPI) technique and focused crawler technology.

Research of Propagation Behavior on Tibetan Network Public Sentiment

In the past some of the social and economic system of the study, often simplified to a single person's behavior can be described using the Poisson process stationary random process. However, since 2005, and reply by email, mail Propagations, human behavior, the actual statistics of time intervals, it was found there with the above assumptions these acts very different characteristics: a long period of silence and short-term high-frequency The outbreak, while present in these human behavior, the time interval distribution also satisfy the inverse power function of the fat tail, that is, during the occurrence of these acts can not be described by Poisson process. In order to study public sentiment of the Tibetan language network generation, transmission and disappearance of the law, it is necessary in the public sentiment research networks in the spread of the main roles. In this paper, the network user behavior patterns in Tibetan, Tibetan text of automatic segmentation, user behavior of the subject property and the main information dissemination and other aspects of behavior modeling are discussed.

On Charactering of Word-of-Mouth Propagation in Heterogeneous Online Social Networks

Recent years have witnessed the explosive growth of research on online social networks (OSNs), which provide a perfect platform for observing the Word-of-Mouth (WOM) propagation. However, recent research regarded OSNs as homogeneous networks, while the real-world OSNs are heterogeneous. This paper considered the heterogeneity of OSNs, proposed a WOM propagation model called HOSN to simulate WOM propagation in different types of nodes in OSNs. Numerical simulation experiments based on agents have been extensively conducted, which show the mechanism of WOM propagation in heterogeneous online social networks. It can be seen from the experimental results that along with the WOM spreading and time passing, more and more users in OSNs review and know the WOM information. Moreover, from the network structure and user behavior perspective, some interesting findings are got. First, the number of initial known nodes has a certain impact on the WOM propagation scope and speed. Then, the number of neighbors has a great impact on the WOM propagation process and results. Finally, percentage of different user type is the decisive factor for WOM propagation either in final propagated number or propagation speed. Findings in this paper attempt to complement the theoretical framework of WOM propagation and complex networks, and thus to further promote the practice of WOM control in OSNs

Structural insights on Internet traffic: Community overlapping and correlations

There is an increasing number of Internet applications, which leads to an increasing network capacity and availability. Internet traffic characterisation and application identification are, therefore, more important for efficient network management. In this paper, we construct flow graphs from detailed Internet traffic data collected from the public networks of Internet Service Providers. We analyse the community structures of the flow graph that is naturally formed by different applications. The community size, degree distribution of the community, and community overlap of 10 Internet applications are investigated. We further study the correlations between the communities from different applications. Our results provide deep insights into the behaviour Internet applications and traffic, which is helpful for both network management and user behaviour analysis.

THE MOBILE SPACE AND MOBILE TARGETING ENVIRONMENT FOR INTERNET USERS: FEATURES OF MODEL SUBMISSION AND USING IN EDUCATION

Article submitted the results of the analysis of the use of mobile devices in education. The substantiation of the definition of user mobility in the Internet space, taking into account the variability of mobile devices and communications. The use of mobile devices in the educational process is based on the paradigm of open and equal access to quality education. Considered the technology of using different types of devices and their functions . The conditions of user mobility in the internet environment, the factors influencing it, the creation and storage of mobile communications resources . Provided with basic mathematical model of user behavior in a virtual network. A model of migration as a user from device to device , and its geographic move , and then use the resulting model for the design of distance learning systems . Preliminary forecasts have been made on the development of education in the transition from the remote technology to open. It is assumed the appearance of new types of personal devices that will combine the power of a desktop PC and the autonomy of smartphones with constant access for broadband wireless connection to the Internet. The use of cloud technology to store and process information resources training helps centralize and synchronize data and access to them from different devices.

A video distribution game with enhanced quality of experience for wireless networks

Video communication over unpredictable wireless medium is a challenging research area. Many mathematical solutions have been proposed by researchers for high-quality video forwarding over such networks. An important example is the game theory. The non-cooperative game theory can be adopted to represent the selfish behaviour of network users. In the current study, a non-cooperative quality game framework (QGF) has been introduced, in which by exploiting a related quality metric, scalable video users try to enhance their perceived quality in a distributed manner in mobile ad-hoc networks. The behaviour of the Nash equilibrium point has been analysed and a distributed resource allocation strategy has been introduced. Also, the quality properties of the proposed QGF under the user churn effects have been investigated. The assigned rates in the quality game can be considered as rate-feedbacks for on-line rate adaptation of a moderate scalable video encoder such as HEVC, H.264/MPEG4 AVC etc. Some experimental results have been presented to validate the theoretical results.

Characterizing User Behavior in a European Academic WiFi Network

Wireless local area networks (WLANs) are commonplace in many universities. Understanding the trends in the usage of these networks is becoming more important. Interesting results can be extracted about association patterns by analyzing WLAN traces from real scenarios. In this work, the library in the main campus of the Technical University of Catalonia (UPC) in Barcelona has been studied. Daily and weekly patterns of the WLAN connections are shown. The population accessing the network is mostly composed of infrequent users: half of the population accesses the WLAN once during each month. Many users associate to only one of the twelve possible access points, which means that, despite the widespread use of lightweight devices, many users are static. The results of this analysis provide general tools for characterizing campus-wide WLAN and a better understanding of usage and performance issues in a mature wireless network.

A novel cache size optimization scheme based on manifold learning in Content Centric Networking

Content Centric Networking (CCN) is an emerging network architecture, shifting from an end-to-end connection to a content centric communication model. Each router in CCN has a content store module to cache the chunks passed by, and is arranged in an arbitrary network topology. It is important to allocate an appropriate cache size to each router in order to both improve the network performance and reduce the economic investment. Previous works have proposed several heterogeneous cache allocation schemes, but the gain brought by these schemes is not obvious. In this paper, we introduce a data mining method into the cache size allocation. The proposed algorithm uses manifold learning to analyze the regularity of network traffic and user behaviors, and classify routers based on their roles in the content delivery. Guided by the manifold learning embedding results, a novel cache size optimization scheme is developed. Extensive experiments have been performed to evaluate the proposed scheme. Simulation results show that the proposed scheme outperforms the existing cache allocation schemes in CCN.