Deep Neural Network (DNN) Solution for Real-time Detection of Distributed Denial of Service (DDoS) Attacks in Software Defined Networks (SDNs)

Makuvaza, Auther, Jat, Dharm Singh and Gamundani, Attlee M., (2021). Deep Neural Network (DNN) Solution for Real-time Detection of Distributed Denial of Service (DDoS) Attacks in Software Defined Networks (SDNs). SN Computer Science, 2(107), n/a-n/a

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  • Sub-type Journal article
    Author Makuvaza, Auther
    Jat, Dharm Singh
    Gamundani, Attlee M.
    Title Deep Neural Network (DNN) Solution for Real-time Detection of Distributed Denial of Service (DDoS) Attacks in Software Defined Networks (SDNs)
    Appearing in SN Computer Science
    Volume 2
    Issue No. 107
    Publication Date 2021-02-20
    Place of Publication online
    Publisher Springer Nature
    Start page n/a
    End page n/a
    Language eng
    Abstract Software-Defined Network (SDN) has emerged as the new big thing in networking. The separation of the control plane from the data plane and application plane gives SDN an edge over traditional networking. With SDN, the devices are configured at the control plane which makes it easier to manage network devices from one central point. However, decoupled architecture creates a single point of failure. A single point of failure attracts cyber-attacks, such as Distributed Denial of Service (DDoS) attacks. Attackers have recently been using multi-vector attacks from single-vector attacks. The need for real-time detection as a countermeasure is of paramount importance. The attackers using sophisticated techniques to launch DDoS attacks dictates the need for a sophisticated intrusion detection system. This paper proposes a Deep Neural Network (DNN) solution for real-time detection of DDoS attacks in SDN. The proposed IDS produced a detection accuracy of 97.59% using fewer resources and less time.
    Copyright Holder The Authors
    Copyright Year 2021
    Copyright type All rights reserved
    ISSN 2662-995X
    DOI 10.1007/s42979-021-00467-1
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    Created: Mon, 26 Aug 2024, 16:37:33 JST by Qian Dai on behalf of UNU CS