Enhancing Network Intrusion Detection in Cloud Computing Using a Deep Boltzmann Machine and LightGBM Ensemble Model: A Performance Evaluation on the NSL-KDD Dataset
Keywords:
LightGBM, Machine Learning, Network Intrusion Detection, Deep Boltzmann Machine, Ensemble ClassificationAbstract
The increasing prevalence of cyber threats and attacks has led to a growing need for network attack analysis. Cloud computing's scalability and adaptability have made it popular among businesses worldwide, but this has also raised concerns about data security. Researchers have been exploring various Intrusion Detection (ID) methodologies over the past few decades. This study proposes a novel approach to classify attacks or abnormalities in network traffic using the NSL-KDD dataset. The proposed model combines a Deep Boltzmann Machine Classifier with an ensemble model of the Light Gradient Boosting technique. By minimizing errors during network intrusion detection, this approach aims to improve the performance of deep learning classifiers. The study evaluates and compares the proposed classifier with established classification strategies, demonstrating superior performance in terms of Recall, F-Measure, Precision, and Accuracy.
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