LSTM-MI: Revolutionizing Intrusion Detection Through Adaptive Learning and Mutual Information Analysis

This study introduces two innovative approaches to augment intrusion detection capabilities. The first method employs Principal Component Analysis (PCA) for dimensionality reduction, streamlining the dataset's complexity while retaining crucial features. The second method delves into feature selection using Mutual Information (MI), identifying and preserving the most informative attributes crucial for intrusion detection. These selected features are then incorporated into LSTM-based models and it evaluates the efficacy of both PCA and MI alongside LSTM, providing a comparative analysis of their impact on intrusion detection performance. Notably, the LSTM-PCA model exhibits an accuracy of 97.73%, while the LSTM-MI model achieves a higher accuracy of 98.21 %. These results underscore the significance of integrating PCA and MI in conjunction with LSTM, offering nuanced insights into their individual contributions to accurate intrusion detection. Through extensive experimentation on benchmark datasets, the proposed methodologies aim to advance the development of robust intrusion detection systems (IDS).