International Journal of Network Management

Cover image for Vol. 27 Issue 3

Edited By: James Won-Ki Hong

Impact Factor: 1.118

ISI Journal Citation Reports © Ranking: 2016: 69/89 (Telecommunications); 107/146 (Computer Science Information Systems)

Online ISSN: 1099-1190

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International Journal of Network Management

The International Journal of Network Management is the forum for researchers, developers, and practitioners in network management to present their work to an international audience. The journal is dedicated to the dissemination of information, which will enable improved management, operation, and maintenance of computer networks and communication systems.

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Recently Published Articles

  1. You have free access to this content
    Special Issue on management of SDN/NFV-based systems

    Rémi Badonnel, Kazuhiko Kinoshita, Daphné Tuncer and Sejun Song

    Version of Record online: 14 JUN 2017 | DOI: 10.1002/nem.1982

  2. Application traffic classification using payload size sequence signature

    Kyu-Seok Shim, Jae-Hyun Ham, Baraka D. Sija and Myung-Sup Kim

    Version of Record online: 1 JUN 2017 | DOI: 10.1002/nem.1981

  3. Dynamic resource allocation for big data streams based on data characteristics (5Vs)

    Navroop Kaur and Sandeep K. Sood

    Version of Record online: 12 MAY 2017 | DOI: 10.1002/nem.1978

    Thumbnail image of graphical abstract

    A novel resource allocation method is proposed that predicts the volume, velocity, variety, veracity, and variability (5Vs) of big data streams and allocate resources accordingly. The experimental results proved the effectiveness of the proposed system.

  4. You have free access to this content
    Special issue on management of the Internet of things and big data

    Jordi Mongay Batalla, George Mastorakis and Constandinos X. Mavromoustakis

    Version of Record online: 10 MAY 2017 | DOI: 10.1002/nem.1971

  5. Botnet behaviour analysis: How would a data analytics-based system with minimum a priori information perform?

    Fariba Haddadi and A. Nur Zincir-Heywood

    Version of Record online: 9 MAY 2017 | DOI: 10.1002/nem.1977

    Thumbnail image of graphical abstract

    This research evaluates the effectiveness of 5 botnet detection systems under various scenarios as well as exploring how a system with minimum a priori information would perform. The evaluation is shown on 24 publicly available botnet data sets. Results indicate that a machine learning–based system with minimum a priori information not only achieves a very high performance but also generalizes much better than the other systems evaluated on a wide range of botnet structures (from centralized to decentralized botnets).

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