CSE-IDS: Using cost-sensitive deep learning and ensemble algorithms to handle class imbalance in network-based intrusion detection systems
In recent times, Network-based Intrusion Detection Systems (NIDSs) have become very
popular for detecting intrusions in computer networks. Existing NIDSs can easily identify …
popular for detecting intrusions in computer networks. Existing NIDSs can easily identify …
[HTML][HTML] Addressing the class imbalance problem in network intrusion detection systems using data resampling and deep learning
A Abdelkhalek, M Mashaly - The journal of Supercomputing, 2023 - Springer
Network intrusion detection systems (NIDS) are the most common tool used to detect
malicious attacks on a network. They help prevent the ever-increasing different attacks and …
malicious attacks on a network. They help prevent the ever-increasing different attacks and …
[HTML][HTML] I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems
Abstract Network-based Intrusion Detection Systems (NIDSs) identify malicious activities by
analyzing network traffic. NIDSs are trained with the samples of benign and intrusive …
analyzing network traffic. NIDSs are trained with the samples of benign and intrusive …
Siam-IDS: Handling class imbalance problem in intrusion detection systems using siamese neural network
To tackle new and complex attacks, modern Intrusion Detection Systems (IDSs) are
developed using Deep Learning (DL) techniques and are trained on intrusion detection …
developed using Deep Learning (DL) techniques and are trained on intrusion detection …
LIO-IDS: Handling class imbalance using LSTM and improved one-vs-one technique in intrusion detection system
Abstract Network-based Intrusion Detection Systems (NIDSs) are deployed in computer
networks to identify intrusions. NIDSs analyse network traffic to detect malicious content …
networks to identify intrusions. NIDSs analyse network traffic to detect malicious content …
[HTML][HTML] HDLNIDS: hybrid deep-learning-based network intrusion detection system
Attacks on networks are currently the most pressing issue confronting modern society.
Network risks affect all networks, from small to large. An intrusion detection system must be …
Network risks affect all networks, from small to large. An intrusion detection system must be …
[HTML][HTML] Advanced feature-selection-based hybrid ensemble learning algorithms for network intrusion detection systems
As cyber-attacks become remarkably sophisticated, effective Intrusion Detection Systems
(IDSs) are needed to monitor computer resources and to provide alerts regarding unusual or …
(IDSs) are needed to monitor computer resources and to provide alerts regarding unusual or …
[HTML][HTML] DCNNBiLSTM: An efficient hybrid deep learning-based intrusion detection system
In recent years, all real-world processes have been shifted to the cyber environment
practically, and computers communicate with one another over the Internet. As a result, there …
practically, and computers communicate with one another over the Internet. As a result, there …
[HTML][HTML] Ensemble classification for intrusion detection via feature extraction based on deep Learning
An intrusion detection system is a security system that aims to detect sabotage and
intrusions on networks to inform experts of the attack and abuse of the network. Different …
intrusions on networks to inform experts of the attack and abuse of the network. Different …
A novel two-stage deep learning model for efficient network intrusion detection
The network intrusion detection system is an important tool for protecting computer networks
against threats and malicious attacks. Many techniques have recently been proposed; …
against threats and malicious attacks. Many techniques have recently been proposed; …