IDSX-Attention: Intrusion detection system (IDS) based hybrid MADE-SDAE and LSTM-Attention mechanism

Abstract

An Intrusion Detection System (IDS) is essential for automatically monitoring cyber-attack activity. Adopting machine learning to develop automatic cyber attack detection has become an important research topic in the last decade. Deep learning is a popular machine learning algorithm recently applied in IDS applications. The adoption of complex layer algorithms in the term of deep learning has been applied in the last five years to increase IDS detection effectiveness. Unfortunately, most deep learning models generate a large number of false negatives, leading to dominant mistake detection that can affect the performance of IDS applications. This paper aims to integrate a statistical Median Absolute Deviation Estimator (MADE) to remove outliers in pre-processing, Stacked Denoising Autoencoder (SDAE) is responsible for reducing data dimensionality, and LSTM-Attention is responsible for producing attack classification tasks. The model was implemented into NSL-KDD dataset and evaluated using Accuracy, F1, Recall, and Confusion metrics. The results showed that the proposed IDSX-Attention outperformed the baseline model, SDAE, LSTM, PCA-LSTM, and Mutual Information (MI)-LSTM, achieving more than a 2% improvement on average. This study demonstrates the potential of the proposed IDSX-Attention, particularly as a deep learning approach, in enhancing the effectiveness of IDS and addressing the challenges in cyber threat detection. It highlights the importance of integrating statistical models, deep learning, and dimensionality reduction mechanisms to improve IDS detection. Further research can explore the integration of other deep learning algorithms and datasets to validate the proposed model's effectiveness and improve the performance of IDS.