Intrusion detection systems (IDSs) play an important role to defend networks from cyber attacks. Among them, anomaly-based IDSs can detect unknown attacks ...

Improving Performance of Anomaly-Based IDS by Combining Multiple Classifiers · An Adaptive Rule-Based Intrusion Alert Correlation Detection Method · Enhancing ...

This proposed model introduces novel deep learning methodologies. The objective here is to create a reliable intrusion detection mechanism to help identify ...

The aim of this paper is to improve the performance of IDS by using ensemble methods and feature selection.

Intrusion detection system (IDS) is a promising cybersecurity research field. ... We have implemented and compared seven classifier machine learning algorithms ...

Dec 26, 2023 · This ensemble voting classifier significantly enhances the accuracy and precision of network intrusion detection systems.

The aim of this paper is to improve the performance of IDS by using ensemble methods and feature selection. The ensemble models were built based on the two ...

Mar 1, 2023 · This study introduces a dual ensemble model by combining two existing ensemble techniques, such as bagging and gradient boosting decision tree (GBDT).

In this paper, by using multiple decision mechanisms, we propose a new classification method to identify misclassified data and then to classify them into three ...

Jun 2, 2019 · We propose a novel intrusion detection model that combines an improved conditional variational AutoEncoder (ICVAE) with a deep neural network (DNN), namely ...