Traffic Measurement for Big Network Data

Traffic Measurement for Big Network Data
Author: Shigang Chen,Min Chen,Qingjun Xiao
Publsiher: Springer
Total Pages: 104
Release: 2016-11-01
Genre: Technology & Engineering
ISBN: 9783319473406

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This book presents several compact and fast methods for online traffic measurement of big network data. It describes challenges of online traffic measurement, discusses the state of the field, and provides an overview of the potential solutions to major problems. The authors introduce the problem of per-flow size measurement for big network data and present a fast and scalable counter architecture, called Counter Tree, which leverages a two-dimensional counter sharing scheme to achieve far better memory efficiency and significantly extend estimation range. Unlike traditional approaches to cardinality estimation problems that allocate a separated data structure (called estimator) for each flow, this book takes a different design path by viewing all the flows together as a whole: each flow is allocated with a virtual estimator, and these virtual estimators share a common memory space. A framework of virtual estimators is designed to apply the idea of sharing to an array of cardinality estimation solutions, achieving far better memory efficiency than the best existing work. To conclude, the authors discuss persistent spread estimation in high-speed networks. They offer a compact data structure called multi-virtual bitmap, which can estimate the cardinality of the intersection of an arbitrary number of sets. Using multi-virtual bitmaps, an implementation that can deliver high estimation accuracy under a very tight memory space is presented. The results of these experiments will surprise both professionals in the field and advanced-level students interested in the topic. By providing both an overview and the results of specific experiments, this book is useful for those new to online traffic measurement and experts on the topic.

Big Data Analytics in Cybersecurity

Big Data Analytics in Cybersecurity
Author: Onur Savas,Julia Deng
Publsiher: CRC Press
Total Pages: 336
Release: 2017-09-18
Genre: Business & Economics
ISBN: 9781498772167

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Big data is presenting challenges to cybersecurity. For an example, the Internet of Things (IoT) will reportedly soon generate a staggering 400 zettabytes (ZB) of data a year. Self-driving cars are predicted to churn out 4000 GB of data per hour of driving. Big data analytics, as an emerging analytical technology, offers the capability to collect, store, process, and visualize these vast amounts of data. Big Data Analytics in Cybersecurity examines security challenges surrounding big data and provides actionable insights that can be used to improve the current practices of network operators and administrators. Applying big data analytics in cybersecurity is critical. By exploiting data from the networks and computers, analysts can discover useful network information from data. Decision makers can make more informative decisions by using this analysis, including what actions need to be performed, and improvement recommendations to policies, guidelines, procedures, tools, and other aspects of the network processes. Bringing together experts from academia, government laboratories, and industry, the book provides insight to both new and more experienced security professionals, as well as data analytics professionals who have varying levels of cybersecurity expertise. It covers a wide range of topics in cybersecurity, which include: Network forensics Threat analysis Vulnerability assessment Visualization Cyber training. In addition, emerging security domains such as the IoT, cloud computing, fog computing, mobile computing, and cyber-social networks are examined. The book first focuses on how big data analytics can be used in different aspects of cybersecurity including network forensics, root-cause analysis, and security training. Next it discusses big data challenges and solutions in such emerging cybersecurity domains as fog computing, IoT, and mobile app security. The book concludes by presenting the tools and datasets for future cybersecurity research.

Big Data Applications in the Telecommunications Industry

Big Data Applications in the Telecommunications Industry
Author: Ouyang, Ye,Hu, Mantian
Publsiher: IGI Global
Total Pages: 216
Release: 2016-12-28
Genre: Computers
ISBN: 9781522517511

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The growing presence of smart phones and smart devices has caused significant changes to wireless networks. With the ubiquity of these technologies, there is now increasingly more available data for mobile operators to utilize. Big Data Applications in the Telecommunications Industry is a comprehensive reference source for the latest scholarly material on the use of data analytics to study wireless networks and examines how these techniques can increase reliability and profitability, as well as network performance and connectivity. Featuring extensive coverage on relevant topics, such as accessibility, traffic data, and customer satisfaction, this publication is ideally designed for engineers, students, professionals, academics, and researchers seeking innovative perspectives on data science and wireless network communications.

Passive and Active Network Measurement

Passive and Active Network Measurement
Author: Constantinos Dovrolis
Publsiher: Springer
Total Pages: 374
Release: 2005-03-31
Genre: Computers
ISBN: 9783540319665

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Welcometothe6thInternationalWorkshoponPassiveandActiveMeasurement, held in Boston, Massuchusetts. PAM 2005 was organized by Boston University, with ?nancial support from Endace Measurement Systems and Intel. PAM continues to grow and mature as a venue for research in all aspects of Internet measurement. This trend is being driven by increasing interest and activity in the ?eld of Internet measurement. To accommodate the increasing interest in PAM, this year the workshop added a Steering Committee, whose members will rotate, to provide continuity and oversight of the PAM workshop series. PAMplaysaspecialroleinthemeasurementcommunity. Itemphasizespr- matic, relevant research in the area of network and Internet measurement. Its focus re?ects the increasing understanding that measurement is critical to e?- tive engineering of the Internet’s components. This is clearly a valuable role, as evidenced by the yearly increases in the number of submissions, interest in, and attendance at PAM. PAM received 84 submissions this year. Each paper was reviewed by three or four Program Committee (PC) members during the ?rst round. Papers that received con?icting scores were further reviewed by additional PC members or external reviewers (typically two). After all reviews were received, each paper with con?icting scores was discussed extensively by its reviewers, until a c- sensus was reached. The PC placed particular emphasis on selecting papers that were fresh and exciting research contributions. Also, strong preference was given to papers that included validation results based on real measurements.

Network Behavior Analysis

Network Behavior Analysis
Author: Kuai Xu
Publsiher: Springer Nature
Total Pages: 163
Release: 2021-12-15
Genre: Computers
ISBN: 9789811683251

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This book provides a comprehensive overview of network behavior analysis that mines Internet traffic data in order to extract, model, and make sense of behavioral patterns in Internet “objects” such as end hosts, smartphones, Internet of things, and applications. The objective of this book is to fill the book publication gap in network behavior analysis, which has recently become an increasingly important component of comprehensive network security solutions for data center networks, backbone networks, enterprise networks, and edge networks. The book presents fundamental principles and best practices for measuring, extracting, modeling and analyzing network behavior for end hosts and applications on the basis of Internet traffic data. In addition, it explains the concept and key elements (e.g., what, who, where, when, and why) of communication patterns and network behavior of end hosts and network applications, drawing on data mining, machine learning, information theory, probabilistic graphical and structural modeling to do so. The book also discusses the benefits of network behavior analysis for applications in cybersecurity monitoring, Internet traffic profiling, anomaly traffic detection, and emerging application detections. The book will be of particular interest to researchers and practitioners in the fields of Internet measurement, traffic analysis, and cybersecurity, since it provides a spectrum of innovative techniques for summarizing behavior models, structural models, and graphic models of Internet traffic, and explains how to leverage the results for a broad range of real-world applications in network management, security operations, and cyber-intelligent analysis. After finishing this book, readers will 1) have learned the principles and practices of measuring, modeling, and analyzing network behavior on the basis of massive Internet traffic data; 2) be able to make sense of network behavior for a spectrum of applications ranging from cybersecurity and network monitoring to emerging application detection; and 3) understand how to explore network behavior analysis to complement traditional perimeter-based firewall and intrusion detection systems in order to detect unusual traffic patterns or zero-day security threats using data mining and machine learning techniques. To ideally benefit from this book, readers should have a basic grasp of TCP/IP protocols, data packets, network flows, and Internet applications.

Intelligence Science and Big Data Engineering

Intelligence Science and Big Data Engineering
Author: Yuxin Peng,Kai Yu,Jiwen Lu,Xingpeng Jiang
Publsiher: Springer
Total Pages: 685
Release: 2018-11-08
Genre: Computers
ISBN: 9783030026981

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This book constitutes the proceedings of the 8th International Conference on Intelligence Science and Big DataEngineering, IScIDE 2018, held in Lanzhou, China, in August 2018.The 59 full papers presented in this book were carefully reviewed and selected from 121 submissions.They are grouped in topical sections on robots and intelligent systems; statistics and learning; deep learning; objects and language; classification and clustering; imaging; and biomedical signal processing.​

Smart Spaces and Next Generation Wired Wireless Networking

Smart Spaces and Next Generation Wired Wireless Networking
Author: Sergey Balandin,Dmitri Moltchanov,Yevgeni Koucheryavy
Publsiher: Springer
Total Pages: 378
Release: 2009-09-03
Genre: Computers
ISBN: 9783642041907

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This book constitutes the refereed proceedings of the 9th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networking, NEW2AN 2009, held in conjunction with the Second Conference on Smart Spaces, ruSMART 2009 in St. Petersburg, Russia, in September 2009. The 32 revised full papers presented were carefully reviewed and selected from a total of 82 submissions. The NEW2AN papers are organized in topical sections on teletraffic issues; traffic measurements, modeling, and control; peer-to-peer systems; security issues; wireless networks: ad hoc and mesh; and wireless networks: capacity and mobility. The ruSMART papers start with an invited talk followed by 10 papers on smart spaces.

Big Data and Knowledge Sharing in Virtual Organizations

Big Data and Knowledge Sharing in Virtual Organizations
Author: Gyamfi, Albert,Williams, Idongesit
Publsiher: IGI Global
Total Pages: 313
Release: 2019-01-25
Genre: Computers
ISBN: 9781522575207

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Knowledge in its pure state is tacit in nature—difficult to formalize and communicate—but can be converted into codified form and shared through both social interactions and the use of IT-based applications and systems. Even though there seems to be considerable synergies between the resulting huge data and the convertible knowledge, there is still a debate on how the increasing amount of data captured by corporations could improve decision making and foster innovation through effective knowledge-sharing practices. Big Data and Knowledge Sharing in Virtual Organizations provides innovative insights into the influence of big data analytics and artificial intelligence and the tools, methods, and techniques for knowledge-sharing processes in virtual organizations. The content within this publication examines cloud computing, machine learning, and knowledge sharing. It is designed for government officials and organizations, policymakers, academicians, researchers, technology developers, and students.