Jan 1, 2021 · This paper proposes the use of deep learning architectures to develop an adaptive and resilient network intrusion detection system (IDS) to detect and classify network attacks.

Jun 14, 2022 · Furthermore, the experiments are performed using two commonly used benchmark intrusion detection datasets: NSL-KDD and KDDCUP'99. The performance of the proposed …

In this paper, we present a hybrid intrusion detection system that combines supervised and unsupervised learning models through an ensemble stacking model to increase the detection …

Nov 24, 2023 · In this study, a new intrusion detection system in a big data environment is developed with a hybrid deep learning algorithm. The algorithm is implemented in Pyspark, …

This research introduces a hybrid detection approach that uses deep learning techniques to improve intrusion detection accuracy and efficiency. The proposed prototype combines the …

Jan 17, 2023 · To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep …

In our research, we took advantage of the Convolutional Neural Network’s ability to extract spatial features and the Long Short-Term Memory Network’s ability to extract temporal features to …

Since Deep Learning ( DL) can derive better representations from the data and construct better models, this work proposes an Intrusion Detection System (IDS) based on DL techniques by …

Jun 1, 2023 · We are motivated by deep learnings exceptional performance in various detection and identification tasks, we present an intelligent and efficient network intrusion detection …

Deep learning algorithms are used automatically to extract essential features from raw network data, which can then be fed into a shallow classifier for effective malicious attack detection.