journal article·2021Open access

Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

Muhammad Reazul Haque, Saw Chin Tan, Zulfadzli Yusoff, Kashif Nisar, Lee Ching Kwang, Rizaludin Kaspin, Bhawani Shankar Chowdhry, Rajkumar Buyya, Satya Prasad Majumder, Manoj Gupta, Shuaib Memon

Computers, Materials & Continua· Volume 68· Issue 3

Abstract

In software-defined networks (SDNs), controller placement is a critical factor in the design and planning for the future Internet of Things (IoT), telecommunication, and satellite communication systems. Existing research has concentrated largely on factors such as reliability, latency, controller capacity, propagation delay, and energy consumption. However, SDNs are vulnerable to distributed denial of service (DDoS) attacks that interfere with legitimate use of the network. The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design, especially in critical applications such as military, health care, and financial services networks requiring high availability. We propose a mathematical model for planning the deployment of SDN smart backup controllers (SBCs) to preserve service in the presence of DDoS attacks. Given a number of input parameters, our model has two distinct capabilities. First, it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating conditions. Second, it recommends an optimal number of smart backup controllers for use with different levels of DDoS attacks. The goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the parameters. Our simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Software-Defined Networks and 5G · Network Security and Intrusion Detection · Smart Grid Security and Resilience

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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