Traffic Anomaly Detection in Wireless Sensor Networks Based on Principal Component Analysis and Deep Convolution Neural Network-Reference-Cited by-同舟云学术

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Traffic Anomaly Detection in Wireless Sensor Networks Based on Principal Component Analysis and Deep Convolution Neural Network

Published:2022 Issue: Volume:10 Page:103136-103149
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Yao Chengpeng¹^ORCID,Yang Yu¹^ORCID,Yin Kun¹^ORCID,Yang Jinwei¹

Affiliation:

1. College of Information Engineering, Engineering University of PAP, Xi’an, China

Funder

Fundamental Research Fund of the School of Information Engineering, Engineering University of PAP

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09903592.pdf?arnumber=9903592

Reference48 articles.

1. An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture;rm;Comput Commun,2020

2. Deep learning approaches for detecting DDoS attacks: a systematic review

3. A lightweight supervised intrusion detection mechanism for IoT networks

4. A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection

5. Network traffic anomaly detection method based on multi scale residual feature;duan;arXiv 2205 03907,2022

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2. LightGuard: A Lightweight Malicious Traffic Detection Method for Internet of Things;IEEE Internet of Things Journal;2024-09-01

3. SDT-IDS: Spatial Data Transformation for Elevating Intrusion Detection Efficiency in IoT Networks;2024 IEEE International Conference on Communications Workshops (ICC Workshops);2024-06-09

4. Abnormal Traffic Detection in Data Mining Techniques using Scaled Exponential Linear Unit based Deep Autoencoders;2024 Second International Conference on Data Science and Information System (ICDSIS);2024-05-17

5. A Hybrid Rider Optimization with Deep Learning Driven Intrusion Detection Farmwork in Wireless Sensor Network;Salud, Ciencia y Tecnología - Serie de Conferencias;2024-05-13

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