过去一年中添加的文章，按日期排序
Hybrid kitchen safety guarding with stove fire recognition based on the Internet of Things
LW Chen, HF Tseng, CY Cho, MF Tsai - Journal of Network and Computer …, 2024 - Elsevier
6 天前 - … a deep learning based fire recognition model integrating densely connected
convolutional networks with neural architecture … In a traditional convolutional neural network with …
convolutional networks with neural architecture … In a traditional convolutional neural network with …
[PDF][PDF] Minimizing time complexity for IOT-IDS based feature selection approach
DH Khamees, EI Essa - computersciencejournals.com
12 天前 - … anomaly detection systems in Internet of Things networks … deep learning techniques
to detect anomalies in IoT … proposed model and feature architecture that provides a robust …
to detect anomalies in IoT … proposed model and feature architecture that provides a robust …
Multi-View Fusion Based for Intrusion Detection with Deep Learning Architectures
M Li, Y Qiao, B Lee - Available at SSRN 4862751 - papers.ssrn.com
14 天前 - … detection systems (IDS) in enterprise Internet of Things (… (AE), convolutional neural
networks (CNN), and deep canonical … to advancing cybersecurity anomaly detection in EIoT …
networks (CNN), and deep canonical … to advancing cybersecurity anomaly detection in EIoT …
An effective classification using enhanced elephant herding optimization with convolution neural network for intrusion detection in IoMT architecture
D Praveena Anjelin, S Ganesh Kumar - Cluster Computing, 2024 - Springer
14 天前 - … algorithms for reliable anomaly detection. The primary … ) for IDSs in Internet of
Things (IoT) networks. This section … intrusion detection system using Deep Neural Networks (…
Things (IoT) networks. This section … intrusion detection system using Deep Neural Networks (…
OPSMOTE-ML: an optimized SMOTE with machine learning models for selective forwarding attack detection in low power and lossy networks of internet of things
18 天前 - … and Deep Learning methods show promise in detecting … The architecture of
the proposed approach comprises … After that, a Convolutional neural network (CNN) was …
the proposed approach comprises … After that, a Convolutional neural network (CNN) was …
[PDF][PDF] Towards Fair and Efficient Distributed Intelligence
29 天前 - … our world through the use of Deep Neural Networks (DNNs). … We construct a
generalized Transformer architecture for … For the anomaly detection task we test the SBT algorithm …
generalized Transformer architecture for … For the anomaly detection task we test the SBT algorithm …
Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection with Vision Transformers
33 天前 - … Compared to traditional deep learning architectures like recurrent neural networks
(RNNs) or … We leverage and optimize machine learning, neural networks, and deep learning …
(RNNs) or … We leverage and optimize machine learning, neural networks, and deep learning …
Internet of Things (IoT)-Enabled Machine Learning Models for Efficient Monitoring of Smart Agriculture
34 天前 - … Furthermore, we introduce a robust anomaly detection … In this section, we use
deep learning models known as … Furthermore, VGG16 deep learning architecture is also …
deep learning models known as … Furthermore, VGG16 deep learning architecture is also …
A Novel Scalable Cloud-enabled Spectrum Sensing Architecture
34 天前 - … complex Machine Learning and Deep Learning based … Sensor Networks (WSNs)
within the Internet of Things (IoT) … the use of Autoencoder networks for anomaly detection […
within the Internet of Things (IoT) … the use of Autoencoder networks for anomaly detection […
IoT Intrusion Detection: A Review of ML and DL-Based Approaches
I Rakine, K El Guemmat, S Ouahabi… - … Research in Applied …, 2024 - ieeexplore.ieee.org
41 天前 - … learning and deep learning-based intrusion detection … : Recurrent Neural Network
(RNN) and Gated Recurrent … overview of IoT architecture, IDS types and detection strategies. …
(RNN) and Gated Recurrent … overview of IoT architecture, IDS types and detection strategies. …