Enhancing Intrusion Detection Using Hybrid Long Short-Term Memory and XGBoost

Abstract

The application of Long Short Term Memory (LSTM) networks in Intrusion Detection Systems (IDS) is a promising area of research that leverages the strengths of deep learning in sequence modeling and anomaly detection. This paper introduces a proposed enhancement to IDS by designing a hybrid model combining LSTM networks and eXtreme Gradient Boosting (XGBoost). The paper aim is to highlight the limitations of traditional IDS methods, such as low detection accuracy and high false-positive rates, by leveraging the complementary strengths of deep learning and gradient-boosted decision trees. The proposed approach improves detection accuracy, reduce false-positive rates, and enhance real-time intrusion detection capabilities, thus providing a robust and efficient solution for network security. The results of the experiments show that the proposed model achieves 98.98% accuracy, 99.03% precision, 99.00% recall and 99.02% f1-score on the testing set. The results approve that the proposed model is outperforming most recently proposed models.