Publication

Abstract : Summary With the use of the Internet, the growth of threats is increasing enormously making it vital to study malware analysis and detection in protecting computer systems and networks from harmful threats. Today, many methodologies employ existing data to predict outcomes for new data points with varying success rates. Machine learning fundamentals suggest that these models be trained with balanced class distributions. However, this does not align with real‐world practices. Most datasets used for identifying malicious threats suffer heavily from class imbalance problems, leading to substantial challenges in the effectiveness of the sampling method. This invites poor classification efficacy subsequently hampering successful classifications. In this chapter, we review different techniques to handle class imbalance problems using machine learning classifiers and various evaluation metrics for other standard datasets, namely, NSL‐KDD, UNSW‐NB15, CIC‐DDoS2019, and Edge‐IIoT.