Conv1D-GRU-Self Attention: An Efficient Deep Learning Framework for Detecting Intrusions in Wireless Sensor Networks

Conv1D-GRU-Self Attention: An Efficient Deep Learning Framework for Detecting Intrusions in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of distributed sensor nodes that collect and transmit environmental data, often in resource-constrained and unsecured environments. These characteristics make WSNs highly vulnerable to various security threats. To address this, the objective of this research i...

Full description

Saved in:
Bibliographic Details
Main Authors: Kenan Honore Robacky Mbongo, Kanwal Ahmed, Orken Mamyrbayev, Guanghui Wang, Fang Zuo, Ainur Akhmediyarova, Nurzhan Mukazhanov, Assem Ayapbergenova
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Future Internet
Subjects:
intrusion detection
Deep Neural Networks (DNNs)
Convolutional Neural Networks (CNNs)
Gated Recurrent Units (GRU)
Self-Attention mechanisms
Online Access:https://www.mdpi.com/1999-5903/17/7/301
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items