Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

Class imbalance occurs when the class distribution is not equal. Namely, one class is under-
represented (minority class), and the other class has significantly more samples in the data …

Network security has become increasingly important in recent decades, while intrusion
detection system plays a critical role in protecting it. Various machine learning techniques …

The threat brought about by Corona Virus or COVID-19 had made a huge impact not only on
the economic, tourism, and health sectors, but it also hardly hit the education system of the …

Abstract Many Intrusion Detection Systems (IDS) has been proposed in the current decade.
To evaluate the effectiveness of the IDS Canadian Institute of Cybersecurity presented a …

In recent years, the availability of time series streaming information has been growing
enormously. Learning from real-time data has been receiving increasingly more attention …

Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …

Applications that generate huge amounts of data in the form of fast streams are becoming
increasingly prevalent, being therefore necessary to learn in an online manner. These …

Data streams are potentially unbounded sequences of instances arriving over time to a
classifier. Designing algorithms that are capable of dealing with massive, rapidly arriving …