Mar 25, 2023 · This paper proposes a deep recurrent neural network (delayed long short-term memory (DLSTM)) intrusion detection model based on the balanced samples.

To solve this problem, this paper researches sampling algorithm and deep learning for intrusion detection in imbalanced network traffic. This paper proposes a ...

A deep recurrent neural network (delayed long short-term memory (DLSTM) intrusion detection model based on the balanced samples that outperforms other ...

This paper proposes a deep recurrent neural network (delayed long short-term memory (DLSTM)) intrusion detection model based on the balanced samples. First, an ...

Sep 18, 2024 · This study proposes a novel approach by hybridizing convolutional neural network (CNN) and gated recurrent unit (GRU) architectures tailored for IoT intrusion ...

Jun 20, 2024 · This strategy significantly improves the model's detection capability while effectively addressing data privacy and security issues. To validate ...

Oct 20, 2024 · This novel hybrid strategy improves cyber security by fusing cutting-edge DL with optimization methods, providing more effective and accurate detection.

Feb 26, 2024 · In order to enhance the performance of network intrusion detection, we propose a sampling method that combines ADASYN with GMM, denoted as AGM.

Oct 5, 2024 · We propose a novel detection model utilizing multi-scale transformer namely IDS-MTran. In essence, the collaboration of multi-scale traffic features broads the ...

Oct 1, 2021 · In this work, we propose a new hybrid DL approach based on the convolutional neural network (CNN) to classify the flow traffic into normal or attack classes.