ArticleScientific reports2025
Enhancing malware detection with feature selection and scaling techniques using machine learning models.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- TL-RL-FusionNet: Reinforcement Learning-Guided Residual MLP with Fused CNN Embeddings for Efficient and Adaptive Ransomware Detection.Sensors (Basel, Switzerland) · 2026Article
- A deep learning-based IoT malware detection approach for electric vehicle charging stations.Scientific reports · 2026Article
- Ensemble machine learning for proactive android ransomware detection using network traffic.Scientific reports · 2026Article
- MH-1M: A 1.34 Million-Sample Multi-Feature Android Malware Dataset with Rich Metadata.Scientific data · 2025Article
- Early classification of functional connectomes in Parkinson's disease: a comparison of machine learning classifiers using multi-scale topological features.BMC medical informatics and decision making · 2025Article
- AI-Driven Cybersecurity in IoT: Adaptive Malware Detection and Lightweight Encryption via TRIM-SEC Framework.Sensors (Basel, Switzerland) · 2025Article
- Adaptive malware identification via integrated SimCLR and GRU networks.Scientific reports · 2025Article
- Crowd Evacuation in Stadiums Using Fire Alarm Prediction.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing prevalence of malware presents a critical challenge to cybersecurity, emphasizing the need for robust detection methods. This study uses a binary tabular classification dataset to evaluate the impact of feature selection, feature scaling, and machine learning (ML) models on malware detection. The methodology involves experimenting with three feature scaling techniques (no scaling, normalization, and min-max scaling), three feature selection methods (no selection, Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA)), and twelve ML models, including traditional algorithms and ensemble methods. A publicly available dataset with 11,598 samples and 139 features is utilized, and model performance is assessed using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Results reveal that the Light Gradient Boosting Machine (LGBM) achieves the highest accuracy of 97.16% when PCA and either min-max scaling or normalization are applied. Additionally, ensemble models consistently outperform traditional ML models, demonstrating their effectiveness in enhancing malware detection. These findings offer valuable insights into optimizing preprocessing and model selection strategies for developing reliable and efficient malware detection systems.
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