Effect of Hyper-Parameter Optimization on the Deep Learning Model Proposed for Distributed Attack Detection in Internet of Things Environment

ABSTRACT This paper studies the effect of various hyper-parameters and their selection for the best performance of the deep learning model proposed in [1] for distributed attack detection in the Internet of Things (IoT). The findings show that there are three hyper-parameters that have more influence on the best performance achieved by the model. As a consequence, this study shows that the model's accuracy as reported in the paper is not achievable, based on the best selections of parameters, which is also supported by another recent publication [2]. INTRODUCTION Diro and Chilamkurti [1] have introduced a distributed deep neural network model for intrusion detection in the IoT environment. The primary principle behind the proposed model is to train the deep neural network model using multiple nodes in a distributed computing environment while parameter sharing and optimization is done through a coordinating master node.