Comprehending SMOTE Adaptations to Alleviate Imbalance in Intrusion Detection Systems

Intrusion detection System (IDS) are an essential component in the world of internet as they identify the malicious activities on the network and alert the network administrators. However, the effectiveness of an IDS is critically hampered due to the inherent imbalanced data problem prevalent in network traffic. An effective solution to this problem has been provided through the technique of Synthetic Minority Over-sampling Technique (SMOTE). This technique brings about balance in the dataset by generating synthetic data points that are similar to the minority class. Many variants of SMOTE have been proposed over the recent years which have been scientifically proven to improve the performance of IDS effectively. This research article provides a comprehensive introduction to the SMOTE (Synthetic Minority Over-sampling Technique) method. It delves into the underlying principles of SMOTE and its effectiveness in improving classification performance when dealing with imbalanced datasets.