[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 …
Intrusion detection system based on fast hierarchical deep convolutional neural network
Currently, with the increasing number of devices connected to the Internet, search for
network vulnerabilities to attackers has increased, and protection systems have become …
network vulnerabilities to attackers has increased, and protection systems have become …
[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 …
[HTML][HTML] A composite approach of intrusion detection systems: hybrid RNN and correlation-based feature optimization
Detection of intrusions is a system that is competent in detecting cyber-attacks and network
anomalies. A variety of strategies have been developed for IDS so far. However, there are …
anomalies. A variety of strategies have been developed for IDS so far. However, there are …
[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 …
A deep learning approach for intrusion detection using recurrent neural networks
C Yin, Y Zhu, J Fei, X He - Ieee Access, 2017 - ieeexplore.ieee.org
Intrusion detection plays an important role in ensuring information security, and the key
technology is to accurately identify various attacks in the network. In this paper, we explore …
technology is to accurately identify various attacks in the network. In this paper, we explore …
[HTML][HTML] A study of network intrusion detection systems using artificial intelligence/machine learning
The rapid growth of the Internet and communications has resulted in a huge increase in
transmitted data. These data are coveted by attackers and they continuously create novel …
transmitted data. These data are coveted by attackers and they continuously create novel …
An intrusion detection model based on feature reduction and convolutional neural networks
Y Xiao, C Xing, T Zhang, Z Zhao - IEEE Access, 2019 - ieeexplore.ieee.org
With the popularity and development of network technology and the Internet, intrusion
detection systems (IDSs), which can identify attacks, have been developed. Traditional …
detection systems (IDSs), which can identify attacks, have been developed. Traditional …
Hybrid optimization and deep learning based intrusion detection system
Abstract Today, Smart City projects and their initiatives are continuously developing with the
vast deployment of the Internet of Things (IoT) devices. Smart cities efficiently manage …
vast deployment of the Internet of Things (IoT) devices. Smart cities efficiently manage …