Applications of Machine Learning in Cyber Security: A Review

Abstract

In recent years, Machine Learning (ML) and Artificial Intelligence (AI) have been gaining ground in Cyber Security (CS) research in an attempt to counter increasingly sophisticated attacks. However, this paper poses the question of qualitative and quantitative data. This paper argues that scholarly research in this domain is severely impacted by the quality and quantity of available data. Datasets are disparate. There is no uniformity in (i) the dataset features, (ii) the methods of collection, or (iii) the preprocessing requirements to enable good-quality analyzed data that are suitable for automated decision-making. This review contributes to the existing literature by providing a single summary of the wider field in relation to AI, evaluating the most recent datasets, combining considerations of ethical AI, and posing a list of open research questions to guide future research endeavors. Thus, this paper contributes valuable insights to the cyber security field, fostering advancements for the application of AI/ML.