ArticleScientific reports2025
Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection.
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 4 papers.
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Who cites it
4 citing papers in PubMed.
- Application of AI in Cyberattack Detection: A Review.Sensors (Basel, Switzerland) · 2026Review
- A dataset-centric review of IoT and IIoT intrusion detection: realism, evaluation biases, and future research directions.Frontiers in big data · 2026Review
- Real-time detection of Wi-Fi attacks using hybrid deep learning models on NodeMCU.Scientific reports · 2025Article
- Optimizing IoT intrusion detection with cosine similarity based dataset balancing and hybrid deep learning.Scientific reports · 2025Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There are serious security issues with the quick growth of IoT devices, which are increasingly essential to Industry 4.0. These gadgets frequently function in challenging environments with little energy and processing power, leaving them open to cyberattacks and making it more difficult to implement intrusion detection systems (IDS) that work. In order to address this issue, this study presents a unique feature selection algorithm based on basic statistical methods and a lightweight intrusion detection system. This methodology improves performance and cuts training time by 27-63% for a variety of classifiers. By utilizing the most discriminative features, the suggested methods lower the computational overhead and improve the detection accuracy. The IDS achieved over 99.9% accuracy, precision, recall, and F1-Score on the dataset IoTID20, with consistent performance on the NSLKDD dataset.
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