A new intrusion detection method using ensemble classification and feature selection DOI Creative Commons

Pooyan Azizi Doost,

Sadegh Sarhani Moghadam,

Edris Khezri

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 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.

Язык: Английский

A new intrusion detection method using ensemble classification and feature selection DOI Creative Commons

Pooyan Azizi Doost,

Sadegh Sarhani Moghadam,

Edris Khezri

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 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.

Язык: Английский

Процитировано

1