ArticleScientific reports2025
An optimal federated learning-based intrusion detection for IoT environment.
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 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed.
- Non-IID and aware federated intrusion detection with PBFT with secured model aggregation for multi institutional healthcare internet of things networks.Scientific reports · 2026Article
- Intrusion Detection in the Internet of Things: A Comprehensive Review of Techniques, Architectures, Datasets, and Emerging Trends.Sensors (Basel, Switzerland) · 2026Review
- Federated Learning and Data Mining-Based Botnet Attack Detection Framework for Internet of Things.Sensors (Basel, Switzerland) · 2026Article
- Application of AI in Cyberattack Detection: A Review.Sensors (Basel, Switzerland) · 2026Review
- AI-driven cybersecurity for industrial internet of things: architectures, challenges, datasets, and future research directions.Frontiers in big data · 2026Review
- Design of an iterative method for adaptive federated intrusion detection for energy-constrained edge-centric 6G IoT cyber-physical systems.Scientific reports · 2025Article
- Graph-based federated learning approach for intrusion detection in IoT networks.Scientific reports · 2025Article
- A comprehensive survey on securing the social internet of things: protocols, threat mitigation, technological integrations, tools, and performance metrics.Scientific reports · 2025Article
- A lightweight framework to secure IoT devices with limited resources in cloud environments.Scientific reports · 2025Article
- Federated learning for crop yield prediction: A comprehensive review of techniques and applications.MethodsX · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Federated Learning (FL) allows the learning models in distributed systems to be trained by sharing the network data and model parameters. The attack patterns of attackers are frequently upgraded as well as the technology improves. Machine learning-based intrusion detection is familiar for cybersecurity in IoT networks. However, these traditional procedures mainly focus on training the machine learning model through specific data and parameters. This might reduce the detection performance of IDS as the system doesn't have insightful knowledge about the new attack patterns. Analyzing and detecting intrusions by analyzing diverse attack patterns is complex for machine learning algorithms. To overcome this, a federated learning-based intrusion detection approach is presented in this research work that trains deep learning classifiers in IoT networks through federated learning and detects different attacks. The Chimp optimization algorithm is used in the proposed work to select optimal features. Experimentations using the benchmark MQTT dataset validate that the FL-based IDS proposed in this research provides a maximum detection accuracy of 95.59% in detecting intrusions over traditional machine learning algorithms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.