With the increasing complexity of cybersecurity threats, Network Intrusion Detection Systems (NIDS) have become essential tools for securing organization networks. These Systems are designed to monitor traffic in real-time and detect unauthorized or malicious activities. Traditional machine learning algorithms have been extensively used for intrusion detection; however, most rely on shallow learning techniques, which are often ineffective in handling high-dimensional and complex network data. This study proposes a deep learning-based intrusion detection framework to address these limitations. The proposed method employs a Deep Belief Network (DBN) for deep feature extraction and dimensionality reduction, followed by a Multi-layer Perceptron (MLP) trained using the backpropagation algorithm to classify and detect intrusions. The approach is evaluated using two benchmark datasets: KDD CUP 1999 and NSL-KDD, selected for their diversity, labeled attack categories, and widespread used IDS performance benchmarking. Experimental results demonstrate that the proposed DBN-BP model achieves an average accuracy of 98.19% on KDD CUP 1999 and 98.17% on NSL-KDD. Experimental results demonstrate that the DBN-MLP approach achieves a recognition rate improvement of 13.45% over traditional SVM-based classifiers, additionally, on the KDD CUP 99 dataset, the DBN-MLP model demonstrates a 12.46% improvement over Decision Tree classifiers. These results confirm the model's superior learning capacity and enhanced ability to generalize to previously unseen attack types. This can be attributed to the hierarchical feature extraction capabilities of the Deep Belief Network combined with the classification strength of the Multi-layer Perceptron. Given these improvements, the DBN-MLP approach is highly suitable for real-time Network Intrusion Detection System (NIDS), especially in enterprise-level and cloud-based environments where high detection accuracy and responsiveness are critical.
Dhafer Alhajim (Mon,) studied this question.