This paper proposes an effective deep learning method, namely AE-IDS (Auto-Encoder Intrusion Detection System) based on random forest algorithm.

This paper proposes an effective deep learning method, namely AE-IDS (Auto-Encoder Intrusion Detection System) based on random forest algorithm.

Apr 2, 2020 � Improving the accuracy of intrusion detection using gar-forest with feature selection, in: Pro- ceedings of the 4th International Conference�...

In this paper, we focus on a two-step approach of feature selection based on Random Forest. The first step selects the features with higher variable importance�...

Feb 21, 2023 � In this work we propose a hybrid IDS which leverages both random forest (RF) and autoencoder (AE). The hybrid model operates in two steps.

Mar 12, 2023 � This study developed a multi-level random forest algorithm for intrusion detection using a fuzzy inference system.

Jan 4, 2022 � An optimum set of features was selected through a Gini Impurity-based Weighted Random Forest (GIWRF) model as the embedded feature selection technique.

Apr 30, 2024 � In this study, we propose a highly efficient model called optimized common features selection and deep-autoencoder (OCFSDA) for lightweight�...

KNN classifier and two kinds of effective feature selection algorithms that include Automatic encoder (Autoencoder) and Principal Component Analysis (PCA)�...

Apr 30, 2024 � In this study, we propose a highly efficient model called optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection�...