Imbalanced data classification: A KNN and generative adversarial networks-based hybrid approach for intrusion detection
With the continuous emergence of various network attacks, it is becoming more and more
important to ensure the security of the network. Intrusion detection, as one of the important …
important to ensure the security of the network. Intrusion detection, as one of the important …
Synthetic attack data generation model applying generative adversarial network for intrusion detection
Detecting a large number of attack classes accurately applying machine learning (ML) and
deep learning (DL) techniques depends on the number of representative samples available …
deep learning (DL) techniques depends on the number of representative samples available …
Addressing imbalanced data problem with generative adversarial network for intrusion detection
Machine learning techniques help to understand underlying patterns in datasets to develop
defense mechanisms against cyber attacks. Multilayer Perceptron (MLP) technique is a …
defense mechanisms against cyber attacks. Multilayer Perceptron (MLP) technique is a …
Effect of balancing data using synthetic data on the performance of machine learning classifiers for intrusion detection in computer networks
Attacks on computer networks have increased significantly in recent days, due in part to the
availability of sophisticated tools for launching such attacks as well as the thriving …
availability of sophisticated tools for launching such attacks as well as the thriving …
A data balancing approach based on generative adversarial network
L Yuan, S Yu, Z Yang, M Duan, K Li - Future Generation Computer Systems, 2023 - Elsevier
Intrusion detection is an effective means of ensuring the proper functioning of industrial
control systems (ICSs). Most intrusion detection algorithms learn the historical ICS data to …
control systems (ICSs). Most intrusion detection algorithms learn the historical ICS data to …
An imbalanced generative adversarial network-based approach for network intrusion detection in an imbalanced dataset
YN Rao, K Suresh Babu - Sensors, 2023 - mdpi.com
In modern networks, a Network Intrusion Detection System (NIDS) is a critical security device
for detecting unauthorized activity. The categorization effectiveness for minority classes is …
for detecting unauthorized activity. The categorization effectiveness for minority classes is …
Deep adversarial learning in intrusion detection: A data augmentation enhanced framework
Intrusion detection systems (IDSs) play an important role in identifying malicious attacks and
threats in networking systems. As fundamental tools of IDSs, learning based classification …
threats in networking systems. As fundamental tools of IDSs, learning based classification …
Network intrusion detection based on conditional Wasserstein generative adversarial network and cost-sensitive stacked autoencoder
G Zhang, X Wang, R Li, Y Song, J He, J Lai - IEEE access, 2020 - ieeexplore.ieee.org
In the field of intrusion detection, there is often a problem of data imbalance, and more and
more unknown types of attacks make detection difficult. To resolve above issues, this article …
more unknown types of attacks make detection difficult. To resolve above issues, this article …
A GAN and Feature Selection‐Based Oversampling Technique for Intrusion Detection
In recent years, there have been numerous cyber security issues that have caused
considerable damage to the society. The development of efficient and reliable Intrusion …
considerable damage to the society. The development of efficient and reliable Intrusion …
AE-CGAN model based high performance network intrusion detection system
JH Lee, KH Park - Applied Sciences, 2019 - mdpi.com
In this paper, a high-performance network intrusion detection system based on deep
learning is proposed for situations in which there are significant imbalances between normal …
learning is proposed for situations in which there are significant imbalances between normal …