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

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].

💡 Research Summary

The paper conducts a systematic investigation of how hyper‑parameter choices affect the performance of a deep‑learning model originally proposed for distributed attack detection in Internet‑of‑Things (IoT) networks. The authors first replicate the architecture described in the reference work