[HTML][HTML] A stacking ensemble of deep learning models for IoT intrusion detection
R Lazzarini, H Tianfield, V Charissis - Knowledge-Based Systems, 2023 - Elsevier
The number of Internet of Things (IoT) devices has increased considerably in the past few
years, which resulted in an exponential growth of cyber attacks on IoT infrastructure. As a …
years, which resulted in an exponential growth of cyber attacks on IoT infrastructure. As a …
[HTML][HTML] Network anomaly detection methods in IoT environments via deep learning: A Fair comparison of performance and robustness
Abstract The Internet of Things (IoT) is a key enabler in closing the loop in Cyber-Physical
Systems, providing “smartness” and thus additional value to each monitored/controlled …
Systems, providing “smartness” and thus additional value to each monitored/controlled …
Network Anomaly Detection Using Autoencoder on Various Datasets: A Comprehensive Review
R Singh, N Srivastava, A Kumar - Recent Patents on …, 2024 - ingentaconnect.com
The scientific community is currently very concerned about information and communication
technology security because any assault or network anomaly can have a remarkable …
technology security because any assault or network anomaly can have a remarkable …
Network traffic anomaly detection method based on multi-scale residual classifier
X Duan, Y Fu, K Wang - Computer Communications, 2023 - Elsevier
In view of the current research seldom consider the multi-scale characteristics of network
traffic, which may lead to an inaccurate classification of anomalies and a high false alarm …
traffic, which may lead to an inaccurate classification of anomalies and a high false alarm …
Scalable anomaly-based intrusion detection for secure Internet of Things using generative adversarial networks in fog environment
W Yao, H Shi, H Zhao - Journal of Network and Computer Applications, 2023 - Elsevier
The data generated exponentially by a massive number of devices in the Internet of Things
(IoT) are extremely high-dimensional, large-scale, non-labeled, which poses great …
(IoT) are extremely high-dimensional, large-scale, non-labeled, which poses great …
基于多尺度特征的网络流量异常检测方法
段雪源， 付钰， 王坤， 刘涛涛， 李彬 - 通信学报, 2022 - infocomm-journal.com
针对传统的网络流量异常检测方法大都只关注流量数据的细粒度特征, 对多尺度特征信息利用不
充分, 可能导致异常检测结果准确率不高的问题, 提出了一种基于多尺度特征的网络流量异常 …
充分, 可能导致异常检测结果准确率不高的问题, 提出了一种基于多尺度特征的网络流量异常 …
Unveiling encrypted traffic types through hierarchical network characteristics
The wide adoption of encrypted traffic brings challenges to network management. Previous
studies propose different approaches to tackle this problem. However, most of them still …
studies propose different approaches to tackle this problem. However, most of them still …
Outlier detection of crowdsourcing trajectory data based on spatial and temporal characterization
X Zheng, D Yu, C Xie, Z Wang - Mathematics, 2023 - mdpi.com
As an emerging type of spatio-temporal big data based on positioning technology and
navigation devices, vehicle-based crowdsourcing data has become a valuable trajectory …
navigation devices, vehicle-based crowdsourcing data has become a valuable trajectory …
Impact of Training Set Size on Resource Usage of Machine Learning Models for IoT Network Intrusion Detection
BA Asulba, N Schumacher, PF Souto… - … Computing in Smart …, 2023 - ieeexplore.ieee.org
Security is a critical concern in Internet-of-Things (IoT) environments, including industrial IoT
and one solution to enhance security is to deploy Network Intrusion Detection Systems …
and one solution to enhance security is to deploy Network Intrusion Detection Systems …