Effect of Hyper-Parameter Optimization on the Deep Learning Model Proposed for Distributed Attack Detection in Internet of Things Environment
This is an incremental study that addresses hyper-parameter tuning for IoT security models, relevant for researchers and practitioners in cybersecurity.
This paper investigates the impact of hyper-parameter optimization on a deep learning model for distributed attack detection in IoT, finding that three hyper-parameters significantly influence performance and that the model's reported accuracy is not achievable with optimal parameter selections.
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].