Oct 1, 2021 · is proposed for network intrusion detection. The model uses information gain (IG) to reduce the dimensionality of high-dimensional data ...
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Oct 1, 2021 · In the present work, a deep belief network model based on information entropy (IE-DBN model) is proposed for network intrusion detection. The ...
Jan 12, 2024 · In the present work, a deep belief network model based on information entropy (IE-DBN model) is proposed for network intrusion detection. The ...
Jul 5, 2022 · In this paper, we develop and evaluate the performance of DBN on detecting cyber-attacks within a network of connected devices. The CICIDS2017 ...
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Peng et al. [42] propose a network intrusion detection method based on deep learning, which uses deep neural network to extract features of network monitoring ...
Here, the intrusion detection model for IoT is designed by using the Taylor-Spider Monkey optimization (Taylor-SMO) which will be developed to train the Deep ...
Deep learning models that have been applied in Intrusion Detection Systems include Deep Belief Networks (DBNs), Convolutional Neural networks (CNNs), Recurrent ...
Apr 4, 2024 · To detect and identify cyber threats, especially in Wi-Fi networks, the research presents FEDDBN-IDS, a revolutionary intrusion detection system ...
Network Anomaly Intrusion Detection Based on Deep Learning Approach
www.ncbi.nlm.nih.gov › PMC9958558
Feb 15, 2023 · In this paper, we used the CSE-CIC-IDS2018 dataset for intrusion detection experiments. Because a large amount of data may cover repeated values ...
May 9, 2022 · This paper analyzes the intrusion threat brought by the power information network and conducts in-depth research and investigation combined with ...