Preprocessed CICIDS2017 Dataset and Reproducible Implementation for Sequence-Based Intrusion Detection using LSTM and Neural ODE Models

This dataset contains the complete experimental resources used for developing and evaluating deep learning–based intrusion detection models on the CICIDS2017 dataset. The objective of this work is to detect network intrusions using sequential deep learning architectures. The study transforms raw network traffic records into fixed-length temporal sequences and evaluates three models: Long Short-Term Memory (LSTM) Euler-based Neural ODE model Runge–Kutta 4 (RK4)-based Neural ODE model