A Deep Learning Model for Intrusion Detection with Imbalanced Dataset

The frequency of cyberattacks increases as network technology evolves and Internet services become more widely utilized. As more individuals and devices connect to the Internet, a substantial amount of traffic data is generated, which facilitates fraudulent activities on these networks. It is essential to have a system that can analyze traffic to prevent criminal activity. Introducing multifaceted IDS approaches to address privacy concerns and security threats using in-depth learning techniques. The performance of deep learning algorithms is highly dependent on the size of the data set and the type of information it contains. Using an unbalanced NSL-KDD data set and model construction steps, we have studied in this article various reduction features and deep learning techniques. LSTM, BiLSTM, and Stacked LSTM are applied to three characteristic reduction methods: Shap values, Boruta, and Anova F-test, in order to compare their results and determine the optimal pair of features to use for the intrusion detection system.