A LSTM-FCNN based multi-class intrusion detection using scalable framework
Abstract Machine learning methods are widely used to implement intrusion detection models
for detecting and classifying intrusions in a network or a system. However, many challenges …
for detecting and classifying intrusions in a network or a system. However, many challenges …
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 …
An intrusion detection approach based on improved deep belief network
Q Tian, D Han, KC Li, X Liu, L Duan, A Castiglione - Applied Intelligence, 2020 - Springer
In today's interconnected society, cyberattacks have become more frequent and
sophisticated, and existing intrusion detection systems may not be adequate in the complex …
sophisticated, and existing intrusion detection systems may not be adequate in the complex …
EIDM: deep learning model for IoT intrusion detection systems
Abstract Internet of Things (IoT) is a disruptive technology for the future decades. Due to its
pervasive growth, it is susceptible to cyber-attacks, and hence the significance of Intrusion …
pervasive growth, it is susceptible to cyber-attacks, and hence the significance of Intrusion …
A one-dimensional convolutional neural network (1D-CNN) based deep learning system for network intrusion detection
The connectivity of devices through the internet plays a remarkable role in our daily lives.
Many network-based applications are utilized in different domains, eg, health care, smart …
Many network-based applications are utilized in different domains, eg, health care, smart …
A new intrusion detection system for the internet of things via deep convolutional neural network and feature engineering
The Internet of Things (IoT) is a widely used technology in automated network systems
across the world. The impact of the IoT on different industries has occurred in recent years …
across the world. The impact of the IoT on different industries has occurred in recent years …
CNN-LSTM: hybrid deep neural network for network intrusion detection system
A Halbouni, TS Gunawan, MH Habaebi… - IEEE …, 2022 - ieeexplore.ieee.org
Network security becomes indispensable to our daily interactions and networks. As attackers
continue to develop new types of attacks and the size of networks continues to grow, the …
continue to develop new types of attacks and the size of networks continues to grow, the …
A novel wide & deep transfer learning stacked GRU framework for network intrusion detection
With the increasing frequency, severity and complexity of recent cyber attacks around the
world, network intrusion detection has become mandatory and highly sophisticated task …
world, network intrusion detection has become mandatory and highly sophisticated task …
Efficient deep CNN-BiLSTM model for network intrusion detection
J Sinha, M Manollas - Proceedings of the 2020 3rd International …, 2020 - dl.acm.org
The need for Network Intrusion Detection systems has risen since usage of cloud
technologies has become mainstream. With the ever growing network traffic, Network …
technologies has become mainstream. With the ever growing network traffic, Network …
[HTML][HTML] Dependable intrusion detection system using deep convolutional neural network: A novel framework and performance evaluation approach
Intrusion detection systems (IDS) play a critical role in safeguarding computer networks
against unauthorized access and malicious activities. However, traditional IDS approaches …
against unauthorized access and malicious activities. However, traditional IDS approaches …