AGPrO-MARL: Adaptive Guiding Prioritized Algorithm for Secure Communication by Suppressing Eavesdropper Activity

Eavesdropping is a malicious snooping that steals sensitive data during wireless communication. Several researchers have undergone the process of suppressing the eavesdropping challenges in the network. However, the existing methods end with certain limitations, such as complexity issues, high-cost consumption, threats occurring in physical layer security systems, low power consumption, and minimal performance effectiveness. To resolve these issues, an Adaptive Guiding Prioritize Optimization-based Multi-Agent Reinforcement Learning (AGPrO-MARL) scheme is developed to improve the communication process by suppressing the eavesdropping problem significantly. The research explores the advances of Adaptive Intelligence Surface (AIS) with the assistance of a multi-agent reinforcement learning scheme to suppress the activity of eavesdropping and secrecy outage. Additionally, the performance effectiveness of the network is improved by the implementation of the AGPrO algorithm, which eliminates the complexity and overlapping challenges significantly. Furthermore, the AIS contains a transmit covariance matrix and a reflecting coefficient function that maximizes the secrecy rate effectively. From this perspective, the proposed model attains better performance under the validation of various evaluation measures, such as throughput, total bytes received, control overhead, and total packets sent. Experimental results indicate that the proposed scheme reports the throughput of 86.25 kb/ms, control overhead of 3.52, total bytes received of 6.22×10 6 , and total packets sent of 16718, while achieving the secrecy rate of 6.44bps/Hz, and secrecy outage probability of 0.029297 with SNR of 30dB in the presence of eavesdropper attacks. Simulation results reveal that the proposed scheme outperforms the other baseline methods with exceptional performance, reporting the high throughput of 97.9 kb/ms, control overhead of 4, total bytes received of 7.06×10 6 , and total packets sent of 18975 without the application of eavesdropper attacks.

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Publication Details

Journal
International Journal of Cooperative Information Systems
Published
2026-09-25
DOI
https://doi.org/10.1142/s0218843026500061
Primary Topic
Wireless Communication Security Techniques
Type
article
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AGPrO-MARL: Adaptive Guiding Prioritized Algorithm for Secure Communication by Suppressing Eavesdropper Activity

S. D. Markande, Anand S. Najan
International Journal of Cooperative Information Systems
Wireless Communication Security Techniques
article

AGPrO-MARL: Adaptive Guiding Prioritized Algorithm for Secure Communication by Suppressing Eavesdropper Activity

S. D. Markande, Anand S. Najan
article en

Abstract

Eavesdropping is a malicious snooping that steals sensitive data during wireless communication. Several researchers have undergone the process of suppressing the eavesdropping challenges in the network. However, the existing methods end with certain limitations, such as complexity issues, high-cost consumption, threats occurring in physical layer security systems, low power consumption, and minimal performance effectiveness. To resolve these issues, an Adaptive Guiding Prioritize Optimization-based Multi-Agent Reinforcement Learning (AGPrO-MARL) scheme is developed to improve the communication process by suppressing the eavesdropping problem significantly. The research explores the advances of Adaptive Intelligence Surface (AIS) with the assistance of a multi-agent reinforcement learning scheme to suppress the activity of eavesdropping and secrecy outage. Additionally, the performance effectiveness of the network is improved by the implementation of the AGPrO algorithm, which eliminates the complexity and overlapping challenges significantly. Furthermore, the AIS contains a transmit covariance matrix and a reflecting coefficient function that maximizes the secrecy rate effectively. From this perspective, the proposed model attains better performance under the validation of various evaluation measures, such as throughput, total bytes received, control overhead, and total packets sent. Experimental results indicate that the proposed scheme reports the throughput of 86.25 kb/ms, control overhead of 3.52, total bytes received of 6.22×10 6 , and total packets sent of 16718, while achieving the secrecy rate of 6.44bps/Hz, and secrecy outage probability of 0.029297 with SNR of 30dB in the presence of eavesdropper attacks. Simulation results reveal that the proposed scheme outperforms the other baseline methods with exceptional performance, reporting the high throughput of 97.9 kb/ms, control overhead of 4, total bytes received of 7.06×10 6 , and total packets sent of 18975 without the application of eavesdropper attacks.

International Journal of Cooperative Information Systems
Twitter (United States) (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 21%
Wireless Communication Security Techniques
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AGPrO-MARL: Adaptive Guiding Prioritized Algorithm for Secure Communication by Suppressing Eavesdropper Activity — S. D. Markande, Anand S. Najan · International Journal of Cooperative Information Systems (2026) | TGRS Research Map | TGRS