Evidence map›Paper›PMID 41162457›Full record

ArticleScientific reports2025

AI-Driven intrusion detection and prevention systems to safeguard 6G networks from cyber threats.

P Chinnasamy, Sarojini Yarramsetti, Ramesh Kumar Ayyasamy, Ella Rajesh, Vijayasaro V, Digvijay Pandey, Binay Kumar Pandey, Mesfin Esayas Lelish

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

P ChinnasamyDepartment of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Srivilliputtur, Tamil Nadu, India.
Sarojini YarramsettiDepartment of Computer Science and Business Systems , Nehru Institute of Engineering and Technology, Coimbatore, 641 105, India. ys10676@gmail.com.
Ramesh Kumar AyyasamyFaculty of Information and Communication Technology , Universiti Tunku Abdul Rahman , Kampar, Perak, Malaysia.
Ella RajeshDepartment of Chemistry, Aditya University, Surampalem, Andhra Pradesh, India.
Vijayasaro VDepartment of ECE, Guru Nanak Institute of Technology, Hyderabad, India.
Digvijay PandeyDepartment of Technical Education Uttar Pradesh, Government of U.P., Lucknow, India. digit11011989@gmail.com.
Binay Kumar PandeyDepartment of Information Technology, College of Technology, Govind Ballabh Pant University of Agriculture and Technology Pantnagar Udham Singh Nagar, Uttarakhand, India. binaydece@gmail.com.
Mesfin Esayas LelishDepartment of Statistics College of Natural and Computational Science , Mizan-Tepi University , Tepi, Ethiopia. mesfinesayas@mtu.edu.et.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sixth-generation (6G) wireless networks, which boast previously unheard-of capacity, reliability, and efficiency, are projected to begin testing and implementation as early as 2030. To meet the demands of new applications, the emphasis is currently on developing 6G networks. The advent of 6G presents additional difficulties, especially in intrusion detection, where sophisticated attacks call for cutting-edge security measures. This research proposes a novel technique using a machine learning algorithm in a 6G network cyber-attack monitoring and intrusion detection system. Here, the 6G network has been monitored, and intrusion detection for cyberattack using blockchain federated Gaussian multi-agent Q-encoder neural networks (BFGMAQENN). Then, the 6G network has been optimized using whale swarm binary wolf optimization (WSBWO). The experimental analysis has been carried out for various cyberattack datasets regarding detection accuracy, data integrity, scalability, communication overhead, and network efficiency. The proposed model attained detection accuracy of 97%, data integrity of 94%, scalability of 93%, communication overhead of 60%, and network efficiency of 98%.

Indexed as

6G networkBinary wolf optimizationCyber-attack monitoringIntrusion detection systemMachine learning algorithm

Identifiers

PMID41162457
PMCPMC12572285

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.