amsys 19(18): e1

Research Article

Denial of Service Attacks Prevention using Traffic Pattern Recognition over Software-Defined Network

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  • @ARTICLE{10.4108/eai.23-3-2018.155334,
        author={Steven Schmitt and Farah I. Kandah},
        title={Denial of Service Attacks Prevention using Traffic Pattern Recognition over Software-Defined Network},
        journal={EAI Endorsed Transactions on Ambient Systems},
        volume={6},
        number={18},
        publisher={EAI},
        journal_a={AMSYS},
        year={2018},
        month={8},
        keywords={Denial of Service attack, Software-defined networks, prevention},
        doi={10.4108/eai.23-3-2018.155334}
    }
    
  • Steven Schmitt
    Farah I. Kandah
    Year: 2018
    Denial of Service Attacks Prevention using Traffic Pattern Recognition over Software-Defined Network
    AMSYS
    EAI
    DOI: 10.4108/eai.23-3-2018.155334
Steven Schmitt1, Farah I. Kandah1,*
  • 1: Computer Science and Engineering Department University of Tennessee at Chattanooga, Chattanooga, TN 37403
*Contact email: farah-kandah@utc.edu

Abstract

Recent trends have shown a migration of software from local machines to server-based services. These service-based networks depend on high up-times and heavy resistance in order to compete in the market. Along with this growth, denial of service attacks have equally grown. Defending against these attacks has become increasingly difficult with the growth of Internet of Things and the different varieties of denial of service attacks. For this, our research offers a solution of implementing software-defined networking and real-time metric based techniques to mitigate a denial of service attack within a smaller time window than other comparable solutions. The use of our method offers both efficient attack handling and also flexibility to fit a variety of implementations. The end result being a network that can automatically adapt against new attacks based on previous network activity.