Multimodal Sensor Data Fusion Based Cyberattack Detection in Industrial Internet of Things Environment-Reference-Cited by-同舟云学术

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Multimodal Sensor Data Fusion Based Cyberattack Detection in Industrial Internet of Things Environment

Published:2023-11-22 Issue: Volume: Page:1656-1661
ISSN:
Container-title:2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA)
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Author:

Nithya R.¹,Sundari J.Jerlin Adaikala²,Rajesh kanna B.³,Balamurugan M. S⁴,Sindhuja R.⁵,Srivastava Aviral⁶

Affiliation:

1. School of Computing, College of Engineering and Technology SRM Institute of Science and Technology,Department of Networking and Communications,Kattankulathur,Tamilnadu

2. Psg College of Arts and Science,Department of Computer Technology,Coimbatore,641014

3. Alagappa Chettiar College of Engineering and Technology,Dept. of Electronics and Communication Engineering,Karaikudi

4. Panimalar Engineering College,Department of ECE,Chennai

5. Saveetha Engineering college,Department of Medical Electronics,Chennai

6. Amity University Rajasthan,Department of CSE,Jaipur,300202

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10394672/10394681/10395001.pdf?arnumber=10395001

Reference19 articles.

1. Increasing the Trustworthiness in the Industrial IoT Networks Through a Reliable Cyberattack Detection Model

2. IMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction

3. An Ensemble of Deep Recurrent Neural Networks for Detecting IoT Cyber Attacks Using Network Traffic

4. Hybrid approach to intrusion detection in fog-based IoT environments

5. A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids

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