ABDNN-IDS: Attention-Based Deep Neural Networks for Intrusion Detection in Industrial IoT
Author:
Affiliation:
1. Prince Sultan University,Riyadh,Saudi Arabia,12435
2. Edinburgh Napier University,School of Computing,Edinburgh,United Kingdom,EH10 5DT
Funder
Prince Sultan University
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10333258/10333344/10333818.pdf?arnumber=10333818
Reference24 articles.
1. A deep learning approach for intrusion detection in Internet of Things using focal loss function
2. Intelligent Intrusion Detection for Industrial Internet of Things Using Clustering Techniques
3. Design of an Industrial IoT-Based Monitoring System for Power Substations
4. Security and Privacy in 5G-IIoT Smart Factories: Novel Approaches, Trends, and Challenges
5. Industrial IoT
Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Securing Industrial IoT Networks Using Conv2D-Attention Approach with Softmax Classifier;2024 IEEE Space, Aerospace and Defence Conference (SPACE);2024-07-22
2. A software vulnerability intelligent detection method based on code association graph;International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024);2024-06-13
3. A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection;IEEE Access;2024
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