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Demo: The Design and Implementation of Intelligent Software Defined Security Framework

Published: 11 October 2019 Publication History

Abstract

Software-defined security (SDS) overcomes the limitations of traditional security mechanisms, which brings significant merits for design, deployment and management. However, existing researches are usually limited to some independent algorithms, while not able to apply multiple algorithms to accommodate various types of attack in actual deployment. In this paper, we propose and implement a novel SDS framework, which aims to flexibly deploy a variety of security functions and artificial intelligence (AI) algorithms to automatically learn ongoing threats and proactively protect the network from attacks.

References

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Sushant Jain, Alok Kumar, Subhasree Mandal, Joon Ong, Leon Poutievski, Arjun Singh, Subbaiah Venkata, Jim Wanderer, Junlan Zhou, Min Zhu, et al. B4: Experience with a globally-deployed software defined wan. In ACM SIGCOMM Computer Communication Review, volume 43, pages 3--14. ACM, 2013.
[2]
Mahmoud Al-Ayyoub, Yaser Jararweh, Elhadj Benkhelifa, Mladen Vouk, Andy Rindos, et al. Sdsecurity: A software defined security experimental framework. In Communication Workshop (ICCW), 2015 IEEE International Conference on, pages 1871--1876. IEEE, 2015.
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Jiaqi Li, Zhifeng Zhao, Rongpeng Li, Honggang Zhang, and Tianhao Zhang. Ai-based two-stage intrusion detection for software defined iot networks. IEEE Internet of Things Journal, 2018.
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Seung Won Shin, Phillip Porras, Vinod Yegneswara, Martin Fong, Guofei Gu, and Mabry Tyson. Fresco: Modular composable security services for software-defined networks. In 20th Annual Network & Distributed System Security Symposium. NDSS, 2013.
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Nasrin Sultana, Naveen Chilamkurti, Wei Peng, and Rabei Alhadad. Survey on sdn based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications, 12(2):493--501, 2019.
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Mohammed A Ambusaidi, Xiangjian He, Priyadarsi Nanda, and Zhiyuan Tan. Building an intrusion detection system using a filter-based feature selection algorithm. IEEE transactions on computers, 65(10):2986--2998, 2016.
[7]
Constantinos Kolias, Georgios Kambourakis, Angelos Stavrou, and Stefanos Gritzalis. Intrusion detection in 802.11 networks: empirical evaluation of threats and a public dataset. IEEE Communications Surveys & Tutorials, 18(1):184--208, 2016.
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OpenStack. Build the future of open infrastructure., 2018.
[9]
OpenDaylight. Home - opendaylight, 2018.

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        cover image ACM Conferences
        MobiCom '19: The 25th Annual International Conference on Mobile Computing and Networking
        August 2019
        1017 pages
        ISBN:9781450361699
        DOI:10.1145/3300061
        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 11 October 2019

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        Author Tags

        1. autodetection
        2. defense
        3. intelligence
        4. software defined security

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        • Demonstration

        Funding Sources

        • Zhejiang Key Research and Development Plan
        • National Natural Science Foundation of China
        • the Fundamental Research Funds for the Central Universities
        • National Key R&D Program of China

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        MobiCom '19
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        Overall Acceptance Rate 440 of 2,972 submissions, 15%

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