Network Packet Classification using Neural Network based on Training Function and Hidden Layer Neuron Number Variation

Distributed denial of service (DDoS) is a structured network attack coming from various sources and fused to form a large packet stream. DDoS packet stream pattern behaves as normal packet stream pattern and very difficult to distinguish between DDoS and normal packet stream. Network packet classification is one of the network defense system in order to avoid DDoS attacks. Artificial Neural Network (ANN) can be used as an effective tool for network packet classification with the appropriate combination of numbers hidden layer neuron and training functions. This study found the best classification accuracy, 99.6% was given by ANN with hidden layer neuron numbers stated by half of input neuron numbers and twice of input neuron numbers but the number of hidden layers neuron by twice of input neuron numbers gives stable accuracy on all training function. ANN with Quasi-Newton training function doesn’t much affected by variation on hidden layer neuron numbers otherwise ANN with Scaled-Conjugate and Resilient-Propagation training function.

@article{Riadi2017,
  title     = {Network Packet Classification using Neural Network based on Training Function and Hidden Layer Neuron Number Variation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {6},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Imam Riadi and Arif Wirawan Muhammad and Sunardi},
  doi       = {10.14569/IJACSA.2017.080631},
  url       = {https://doi.org/10.14569/IJACSA.2017.080631}
}