Enhancing malware detection with feature selection and scaling techniques using machine learning models

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 17, 2025

Abstract The increasing prevalence of malware presents a critical challenge to cybersecurity, emphasizing the need for robust detection methods. This study uses binary tabular classification dataset evaluate impact feature selection, scaling, and machine learning (ML) models on detection. methodology involves experimenting with three scaling techniques (no normalization, min-max scaling), selection methods Linear Discriminant Analysis (LDA), Principal Component (PCA)), twelve ML models, including traditional algorithms ensemble A publicly available 11,598 samples 139 features is utilized, model performance assessed using metrics such as accuracy, precision, recall, F1-score, AUC-ROC. Results reveal that Light Gradient Boosting Machine (LGBM) achieves highest accuracy 97.16% when PCA either or normalization are applied. Additionally, consistently outperform demonstrating their effectiveness in enhancing These findings offer valuable insights into optimizing preprocessing strategies developing reliable efficient systems.

Language: Английский

A new intrusion detection method using ensemble classification and feature selection

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 20, 2025

Intrusion Detection Systems (IDS) play a crucial role in ensuring network security by identifying and mitigating cyber threats. This study introduces hybrid intrusion detection approach that integrates Convolutional Neural Networks (CNNs) for feature extraction the Random Forest (RF) algorithm classification. The proposed method enhances accuracy leveraging CNNs to automatically extract relevant features, reducing data dimensionality noise. Subsequently, RF classifier processes these optimized features achieve robust precise To evaluate effectiveness of approach, experiments were conducted on KDD99 UNSW-NB15 datasets. results demonstrate model achieves an 97% precision over 98%, outperforming traditional machine learning-based IDS solutions. These findings highlight potential framework as scalable efficient cybersecurity solution real-world environments.

Language: Английский

Allocation of computing resources based on multi-objective strategy and performance improvement in 5G networks

Computer Communications, Journal Year: 2025, Volume and Issue: unknown, P. 108197 - 108197

Published: April 1, 2025

Language: Английский