ArticleScientific reports2025
Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks.
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 5 papers.
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Who cites it
5 citing papers in PubMed.
- Dynamic multi-strategy Grey Wolf optimizer and its applications.Scientific reports · 2026Article
- A hybrid improved binary GWO-PSO with random forest (IBGWO-PSO-RF) based intrusion detection model for large-scale attacks in IoT environment.Scientific reports · 2026Article
- An intelligent deep representation learning with enhanced feature selection approach for cyberattack detection in internet of things enabled cloud environment.Scientific reports · 2025Article
- Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest.Scientific reports · 2025Article
- Artificial intelligence-driven cybersecurity system for internet of things using self-attention deep learning and metaheuristic algorithms.Scientific reports · 2025Article
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Authors and funding
3 authors.
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Abstract
Smart devices are enabled via the Internet of Things (IoT) and are connected in an uninterrupted world. These connected devices pose a challenge to cybersecurity systems due attacks in network communications. Such attacks have continued to threaten the operation of systems and end-users. Therefore, Intrusion Detection Systems (IDS) remain one of the most used tools for maintaining such flaws against cyber-attacks. The dynamic and multi-dimensional threat landscape in IoT network increases the challenge of Traditional IDS. The focus of this paper aims to find the key features for developing an IDS that is reliable but also efficient in terms of computation. Therefore, Enhanced Grey Wolf Optimization (EGWO) for Feature Selection (FS) is implemented. The function of EGWO is to remove unnecessary features from datasets used for intrusion detection. To test the new FS technique and decide on an optimal set of features based on the accuracy achieved and the feature taking filters, the most recent FS approach relies on the NF-ToN-IoT dataset. The selected features are evaluated by using the Random Forest (RF) algorithm to combine multiple decision trees and create an accurate result. The experimental outcomes against the most recent procedures demonstrate the capacity of the recommended FS and classification methods to determine attacks in the IDS. Analysis of the results presents that the recommended approach performs more effectively than the other recent techniques with optimized features (i.e., 23 out of 43 features), high accuracy of 99.93% and improved convergence.
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