A new intrusion detection method using ensemble classification and feature selection

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.