Detection of Distributed Denial of Service (DDoS) attacks using convolutional neural networks

  • A. O. Akinwumi
  • A. O. Akingbesote
  • O. O. Ajayi
  • F. O. Aranuwa
Keywords: Distributed Denial of Service, Convolutional Neural Networks, Network Traffic, Network Security, Machine Learning, Cybercrime


The rapid evolution of the Internet has brought tremendous benefits to the world at large. To effectively leverage on the importance of the internet, there is the need for a secured and reliable network. But currently, there are lots of network attacks against network infrastructures. One of such attack is the Distributed Denial of Service (DDoS) attacks which is an attempt by hackers to deny authorized users access internet service availability using many attack machines. In this paper, a Convolutional Neural Network based detection model is proposed to proffer solution to the challenges of DDoS attacks. The dataset for the modelling was sourced from the KDD Cup-99 Dataset. The evaluation of the experiment conducted was based on three standard metrics of accuracy, sensitivity and specificity. The experimental results showed that the developed model had an accuracy of 99.72%, specificity of 99.69% and sensitivity of 99.71%. Furthermore, the performance of the model was compared with other existing traditional learning models, the results indicated that the model presented in this work performed significantly better.


Journal Identifiers

eISSN: 2467-8821
print ISSN: 0331-8443