ArticleScientific reports2025
A new intrusion detection method using ensemble classification and 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 3 papers.
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Who cites it
3 citing papers in PubMed.
- A hybrid spiking convolutional neural framework with extreme learning machine for enhanced anomaly detection in network security.Scientific reports · 2026Article
- MultiNet-IDS: an ensemble of DRL and LSTM for improved intrusion detection in wireless sensor networks.Scientific reports · 2025Article
- Fault-tolerant and mobility-aware loading via Markov chain in mobile cloud computing.Scientific reports · 2025Article
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5 authors.
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Abstract
Intrusion Detection Systems (IDS) play a crucial role in ensuring network security by identifying and mitigating cyber threats. This study introduces a hybrid intrusion detection approach that integrates Convolutional Neural Networks (CNNs) for feature extraction and the Random Forest (RF) algorithm for classification. The proposed method enhances detection accuracy by leveraging CNNs to automatically extract relevant network features, reducing data dimensionality and noise. Subsequently, the RF classifier processes these optimized features to achieve robust and precise intrusion classification. To evaluate the effectiveness of the approach, experiments were conducted on the KDD99 and UNSW-NB15 datasets. The results demonstrate that the proposed model achieves an accuracy of 97% and a precision of over 98%, outperforming traditional machine learning-based IDS solutions. These findings highlight the potential of the proposed hybrid framework as a scalable and efficient cybersecurity solution for real-world network environments.
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