An Intelligent Intrusion Detection System Using Deep Learning and Multi-modal Fusion with RF Classification (Samarra Journal of Pure and Applied Science, 2026)
The urgent demand for effective and intelligent intrusion detection systems (IDSs) to handle this problem has grown in line with the growing complexity and diversity of cyberattacks. With deep learning (DL) methods, the presented study aims to build an integrated framework that raises the intrusion detection accuracy and generalizability across several data sources. In this work, three well-known datasets, UNSW-NB15, NSL-KDD, and CICIDS2017, have been utilized. The suggested approach uses convolutional neural networks (CNNs) to extract deep features from such datasets. Dimensionality is reduced and irrelevant features are eliminated using principal component analysis (PCA). Combining the three datasets using a multimodal deep autoencoder (MDAE) extracts a new dataset. Final classification utilizes random forest (RF) technology, efficiently and precisely classifying network traffic using combined data. Supported by high precision, F1 score, and recall of 99.9%, evaluation findings show an excellent accuracy of the model of 99.9%. These results show how well modern classification algorithms, feature extraction, and multi-modal data fusion methods can be combined to create a sophisticated intrusion detection model capable of efficiently and consistently defending against challenging attacks.