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

Authors: Md Mohaimenuzzaman, Zahraa Said Abdallah, Joarder Kamruzzaman, Bala Srinivasan
Published: 2018-06-19

Source: https://arxiv.org/abs/1806.07057

PDF: https://arxiv.org/pdf/1806.07057

AIにより推定されたラベル

深層学習技術 サイバーセキュリティ

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
詳細は文献データベースについてをご覧ください。

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].