Drift Dataset Generator for Evaluating Flow-Based Intrusion Detection Systems

In this work, we present a framework called Drift Dataset Generator for Evaluating Flow-Based IDS (Drift-IDS-Generator). Our framework introduces drifts into public IDS datasets, trains intrusion detection models using data stream classification algorithms, and evaluates their performance to better understand how these algorithms respond to different types of concept drift, making the evaluation process more realistic. We also conducted a case study using two types of attacks from the CIC-IDS2017 dataset and found that even when applying the same data stream classification algorithm, performance varied depending on the type of drift.