Optimizing Deep Learning Model Hyperparameters for Botnet Attack
Detection in IoT Networks
Abstract
Deep Learning (DL) models can be trained to automatically learn the
underlying features of the traffic patterns in IoT networks to detect
complex botnet attacks. However, the performance of a neural network
model largely depends on the set of hyperparameters that is used for the
model development. In this paper, an algorithm is proposed to determine
the optimal set of hyperparameters (the numbers of hidden layers and
hidden units, the learning rate, the optimiser, the activation function,
the batch size, and the number of epochs) for efficient DL-based botnet
detection in IoT networks. The DL models employ a Deep Neural Network
(DNN) architecture for binary and multi-class classification. DNN-based
botnet detection models are developed and experiments are performed with
the Bot-IoT and N-BaIoT datasets to validate the effectiveness of the
hyperparameter optimisation method. Experiment results showed that the
proposed method produced DNN models that achieved high botnet attack
detection rates, low false alarm rates, and near real-time computation
speed.