[HTML][HTML] Using a long short-term memory recurrent neural network (LSTM-RNN) to classify network attacks
An intrusion detection system (IDS) identifies whether the network traffic behavior is normal
or abnormal or identifies the attack types. Recently, deep learning has emerged as a …
or abnormal or identifies the attack types. Recently, deep learning has emerged as a …
An intrusion detection system using a deep neural network with gated recurrent units
C Xu, J Shen, X Du, F Zhang - IEEE Access, 2018 - ieeexplore.ieee.org
To improve the performance of network intrusion detection systems (IDS), we applied deep
learning theory to intrusion detection and developed a deep network model with automatic …
learning theory to intrusion detection and developed a deep network model with automatic …
[HTML][HTML] Network intrusion detection based on novel feature selection model and various recurrent neural networks
The recent increase in hacks and computer network attacks around the world has intensified
the need to develop better intrusion detection and prevention systems. The intrusion …
the need to develop better intrusion detection and prevention systems. The intrusion …
DL‐IDS: extracting features using CNN‐LSTM hybrid network for intrusion detection system
P Sun, P Liu, Q Li, C Liu, X Lu, R Hao… - Security and …, 2020 - Wiley Online Library
Many studies utilized machine learning schemes to improve network intrusion detection
systems recently. Most of the research is based on manually extracted features, but this …
systems recently. Most of the research is based on manually extracted features, but this …
Attack classification of an intrusion detection system using deep learning and hyperparameter optimization
A network intrusion detection system (NIDS) is a solution that mitigates the threat of attacks
on a network. The success of a NIDS depends on the success of its algorithm and the …
on a network. The success of a NIDS depends on the success of its algorithm and the …
A novel two-stage deep learning model for network intrusion detection: LSTM-AE
Machine learning and deep learning techniques are widely used to evaluate intrusion
detection systems (IDS) capable of rapidly and automatically recognizing and classifying …
detection systems (IDS) capable of rapidly and automatically recognizing and classifying …
[HTML][HTML] A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework
SM Kasongo - Computer Communications, 2023 - Elsevier
In recent years, the spike in the amount of information transmitted through communication
infrastructures has increased due to the advances in technologies such as cloud computing …
infrastructures has increased due to the advances in technologies such as cloud computing …
[HTML][HTML] Deep neural network based real-time intrusion detection system
SP Thirimanne, L Jayawardana, L Yasakethu… - SN Computer …, 2022 - Springer
In recent years, due to the rapid growth in network technology, numerous types of intrusions
have been uncovered that differ from the existing ones, and the conventional firewalls with …
have been uncovered that differ from the existing ones, and the conventional firewalls with …
Robust network intrusion detection scheme using long-short term memory based convolutional neural networks
The intrusion detection system (IDS) is a crucial part in the network administration system to
detect some types of cyber attack. IDS is categorized as a classifying machine thus it is likely …
detect some types of cyber attack. IDS is categorized as a classifying machine thus it is likely …
A comparative study of machine learning classifiers for network intrusion detection
The network intrusion detection system (NIDS) has become an essential tool for detecting
attacks in computer networks and protecting the critical information and systems. The …
attacks in computer networks and protecting the critical information and systems. The …