Secure routing with multi-watchdog construction using deep particle convolutional model for IoT based 5G wireless sensor networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F22%3A43905000" target="_blank" >RIV/60076658:12310/22:43905000 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S014036642200041X?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S014036642200041X?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.comcom.2022.02.004" target="_blank" >10.1016/j.comcom.2022.02.004</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Secure routing with multi-watchdog construction using deep particle convolutional model for IoT based 5G wireless sensor networks

Popis výsledku v původním jazyce

Fifth Generation (5G) security principles are widely expected with effective cryptography models, information security models, Machine Learning (ML) based Intrusion Detection systems (IDS) for Internet of Things (IoT) based Wireless Sensor Networks (WSN). However, the current security models are insufficient against the dynamic network nature of WSNs. On this scope, the proposed system develops Deep Convolutional Neural Network (DCNN) and Distributed Particle Filtering Evaluation Scheme (DPFES) for constructing a secure and cooperative multi-watchdog system. The proposed Deep Learning (DL) based dynamic multi-watchdog system protects each sensor node by monitoring the node transmission. In addition, the proposed work encompasses secure data-centric and node-centric evaluation procedures that are required for expanding the secure medium of 5G-based IoT-WSN networks. The DL-based network evaluation procedures drive the entire network to build a secure multi-watchdog system that enables on-demand active watchdog IDS agents among dense IoT-WSN. Notably, the proposed work contains a system dynamics model, cooperative watchdog model, Dual Line Minimum Connected Dominating Set (DL-MCDS), and DL-based event analysis procedures. Based on technical aspects, the proposed system is motivated to implement DPFES to analyze network events using particle filtering frameworks to build a secure 5G environment. The system is implemented and results are compared with related works. The performance of the proposed cooperative multi-watchdog system delivers 10% and 15% of better results than other techniques.

Název v anglickém jazyce

Secure routing with multi-watchdog construction using deep particle convolutional model for IoT based 5G wireless sensor networks

Popis výsledku anglicky

Fifth Generation (5G) security principles are widely expected with effective cryptography models, information security models, Machine Learning (ML) based Intrusion Detection systems (IDS) for Internet of Things (IoT) based Wireless Sensor Networks (WSN). However, the current security models are insufficient against the dynamic network nature of WSNs. On this scope, the proposed system develops Deep Convolutional Neural Network (DCNN) and Distributed Particle Filtering Evaluation Scheme (DPFES) for constructing a secure and cooperative multi-watchdog system. The proposed Deep Learning (DL) based dynamic multi-watchdog system protects each sensor node by monitoring the node transmission. In addition, the proposed work encompasses secure data-centric and node-centric evaluation procedures that are required for expanding the secure medium of 5G-based IoT-WSN networks. The DL-based network evaluation procedures drive the entire network to build a secure multi-watchdog system that enables on-demand active watchdog IDS agents among dense IoT-WSN. Notably, the proposed work contains a system dynamics model, cooperative watchdog model, Dual Line Minimum Connected Dominating Set (DL-MCDS), and DL-based event analysis procedures. Based on technical aspects, the proposed system is motivated to implement DPFES to analyze network events using particle filtering frameworks to build a secure 5G environment. The system is implemented and results are compared with related works. The performance of the proposed cooperative multi-watchdog system delivers 10% and 15% of better results than other techniques.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computer Communication

ISSN

0140-3664

e-ISSN

1873-703X

Svazek periodika

187

Číslo periodika v rámci svazku

APR 1 2022

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

71-82

Kód UT WoS článku

000817094300006

EID výsledku v databázi Scopus

2-s2.0-85124704537