Harnessing Machine Learning for Effective Cyber security Classifiers

Abstract

Machine learning has emerged as a transformative force, innovating diverse industries through its capacity to infuse meaningful insights from large datasets. It plays a pivotal role in powering data analysis, discover pattern matching, identifying hidden or evolving risks in securing systems. The ability of categorizing and behavior analysis is central to its efficacy in cybersecurity. This paper highlights the importance of machine learning in landscape of cyber threats. In this paper, we have identified few machine learning algorithms to categorize huge dataset. The complexities of identifying hidden risks increases by many folds, when the input data is voluminous. Evaluating and contemplating the underlying meaning of data is time-consuming and can be missed easily. We compared different types of machine learning algorithms. Each machine learning algorithm has its strength and weakness. It is found that, the TressJ48 algorithm is proficient in classifying the large dataset, better than Naive Bayes and Decision Stump algorithms. The efficient classifier helps to generate insight, which can be further used to make decisions in terms of cybersecurity.