A Deep Learning Approach for the Detection of Intrusions with an Ensemble Feature Selection Method

Intrusion detection is essential for preserving computer network security and dependability, particularly in an ever-increasing cyber-attack era. Conventional research techniques rely on specific features and machine learning approaches that can’t detect complex patterns and irregularities in network communications data. In this article, we provide a unique technique for intrusion detection which integrates ensemble feature extraction methods with Long Short Term Memory networks. To evaluate the efficacy of the suggested technique, we used benchmark network intrusion datasets (NSL-KDD and UNSW-NB15) and compared them to other available approaches. Performance indicators such as recall, F1-score, precision, and accuracy are utilized to evaluate how well the algorithm detects intrusions. The research findings indicate that ensemble feature selection using the proposed algorithm surpasses other approaches regarding accuracy and robustness. The method provided 99.56%, 98.14% of accuracy for binary classification and 98.05% and 97.47% of accuracy for multi-class classification for both NSL-KDD and UNSW-NB15 datasets. These results demonstrate the ability of ensemble-based approaches to improve the efficiency of intrusion detection devices in real-world applications.