Benchmarking of Machine Learning for Anomalybased Intrusion Detection Systems Using LSTM-RNN

Over the past few years, the challenge has been the increasing and significant attacks on anomaly detection processes. While attacks in anomaly detection can be easily predicted using Intrusion Detection Systems (IDS), the accuracy of the prediction process remains low. To address the issues associated with Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for IDS detection. Furthermore, Z-score normalization aims to eliminate duplicate data and minimize unknown data during the preprocessing stage. Additionally, the Grasshopper Optimization Algorithm is used to select relevant features from the margin. Behavior analysis is employed to verify each type of data in a prediction dataset and identify the necessary checks at each performance stage. Finally, the proposed method evaluates testing and training values, classifies intrusions, and detects various attacks in the early stages. The proposed technique reduces time complexity and improves the accuracy to 93%.