Advanced Q-Learning-Based Dynamic Key Distribution for Secure Wireless Communication IoT Networks

Secure IoT network-based dynamic key distribution using Q-learning techniques. IoT networks play a vital role in contemporary wireless communication systems, so developing efficient security mechanisms is crucial in a world where the number of smart devices is increasing dramatically. Reinforcement learning based solution However, this paper proposed a reinforcement learning based solution, in which a Q-learning based key distribution approaches is presented to adapt with the changing security requirements of different IoT devices. Here, each IoT device learns to broadcast keys according to multi-agent reinforcement learning problem to prevent threats and not only this but improve overall network security. We validate the method through extensive simulations showing significant improvements in performance on key management and threat mitigation. Our findings indicate that the shrewd integration of dynamic key distribution with reinforcement learning can serve as a most effective approach to safeguard contemporary IoT ecosystems from diverse cyber-attacks.

Authors

Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2025-04-21
DOI
https://doi.org/10.52783/jisem.v10i38s.6962
Primary Topic
Wireless Communication Security Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Advanced Q-Learning-Based Dynamic Key Distribution for Secure Wireless Communication IoT Networks

Mohammed Aboud Kadhim
Journal of Information Systems Engineering & Management
Wireless Communication Security Techniques
article

Advanced Q-Learning-Based Dynamic Key Distribution for Secure Wireless Communication IoT Networks

Mohammed Aboud Kadhim
article en

Abstract

Secure IoT network-based dynamic key distribution using Q-learning techniques. IoT networks play a vital role in contemporary wireless communication systems, so developing efficient security mechanisms is crucial in a world where the number of smart devices is increasing dramatically. Reinforcement learning based solution However, this paper proposed a reinforcement learning based solution, in which a Q-learning based key distribution approaches is presented to adapt with the changing security requirements of different IoT devices. Here, each IoT device learns to broadcast keys according to multi-agent reinforcement learning problem to prevent threats and not only this but improve overall network security. We validate the method through extensive simulations showing significant improvements in performance on key management and threat mitigation. Our findings indicate that the shrewd integration of dynamic key distribution with reinforcement learning can serve as a most effective approach to safeguard contemporary IoT ecosystems from diverse cyber-attacks.

Journal of Information Systems Engineering & ManagementVol. 10(38s)
Openalex Percentile: Top 93%
Wireless Communication Security Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.