A data-driven network intrusion detection system using feature selection and deep learning
L Zhang, K Liu, X Xie, W Bai, B Wu, P Dong - Journal of Information Security …, 2023 - Elsevier
Network intrusion detection system (NIDS) is an important line of defense for network
security as network attacks become more frequent. In this paper, we propose a data-driven …
security as network attacks become more frequent. In this paper, we propose a data-driven …
Resmatch: Referring expression segmentation in a semi-supervised manner
Referring Expression segmentation (RES), a task that involves localizing specific instance-
level objects on the basis of free-form linguistic descriptions, has emerged as a crucial …
level objects on the basis of free-form linguistic descriptions, has emerged as a crucial …
Ensembled masked graph autoencoders for link anomaly detection in a road network considering spatiotemporal features
Road anomaly detection aims to find a small group of roads that are exceptional with respect
to the rest of the physical links in a transportation network, posing great challenges for …
to the rest of the physical links in a transportation network, posing great challenges for …
[HTML][HTML] Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm
The intrusion detection system (IDS) model, which can identify the presence of intruders in
the network and take some predefined action for safe data transit across the network, is …
the network and take some predefined action for safe data transit across the network, is …
Learning multiple gaussian prototypes for open-set recognition
Open-set recognition aims to deal with unknown classes that do not exist in the training
phase. The key is to learn effective latent feature representations for classifying the already …
phase. The key is to learn effective latent feature representations for classifying the already …
Unsupervised anomaly detection using inverse generative adversarial networks
Unsupervised anomaly detection (UAD) holds considerable promise for a myriad of real-
world applications related to information sciences. Recently, generative models, such as …
world applications related to information sciences. Recently, generative models, such as …
A multi-information fusion anomaly detection model based on convolutional neural networks and AutoEncoder
Z Zhao, H Guo, Y Wang - Scientific Reports, 2024 - nature.com
Network traffic anomaly detection, as an effective analysis method for network security, can
identify differentiated traffic information and provide secure operation in complex and …
identify differentiated traffic information and provide secure operation in complex and …
A Gramian angular field-based data-driven approach for multiregion and multisource renewable scenario generation
Scenario generation is a pivotal method for providing system operators with a reasonable
quantity of power scenarios that are capable of reflecting uncertainties and spatiotemporal …
quantity of power scenarios that are capable of reflecting uncertainties and spatiotemporal …
Unsupervised Anomaly Detection on Attributed Networks With Graph Contrastive Learning for Consumer Electronics Security
The proliferation of consumer electronic products has engendered a substantial surge in
data generation and information exchange, concurrently escalating the potential for security …
data generation and information exchange, concurrently escalating the potential for security …
TMANomaly: Time-Series Mutual Adversarial Networks for Industrial Anomaly Detection
L Zhang, W Bai, X Xie, L Chen… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Large-scale sewage treatment plants are one of the typical Industrial Internet of Things
systems, where the presence of a large number of sensors generates massive dynamic time …
systems, where the presence of a large number of sensors generates massive dynamic time …