IFACNN: efficient DDoS attack detection based on improved firefly algorithm to optimize convolutional neural networks.

Journal: Mathematical biosciences and engineering : MBE
Published Date: Dec 2, 2021

Abstract

Network security has become considerably essential because of the expansion of internet of things (IoT) devices. One of the greatest hazards of today's networks is distributed denial of service (DDoS) attacks, which could destroy critical network services. Recent numerous IoT devices are unsuspectingly attacked by DDoS. To securely manage IoT equipment, researchers have introduced software-defined networks (SDN). Therefore, we propose a DDoS attack detection scheme to secure the real-time in the software-defined the internet of things (SD-IoT) environment. In this article, we utilize improved firefly algorithm to optimize the convolutional neural network (CNN), to provide detection for DDoS attacks in our proposed SD-IoT framework. Our results demonstrate that our scheme can achieve higher than 99% DDoS behavior and benign traffic detection accuracy.

Authors

  • Jiushuang Wang
    National Engineering Laboratory on Interconnection Technology for Next Generation Internet, Beijing Jiaotong University, Beijing, China.
  • Ying Liu
    The First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
  • Huifen Feng
    Department of Gastroenterology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People's Republic of China.

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