Open Access

REVIEW

Enhancing Cyber Security through Artificial Intelligence and Machine Learning: A Literature Review

by Carlos Merlano^*

Purdue Polytechnic Institute, Purdue University, West Lafayette, IN 47907, USA

* Corresponding Author: Carlos Merlano. Email:

Journal of Cyber Security 2024, 6, 89-116. https://doi.org/10.32604/jcs.2024.056164

Received 15 July 2024; Accepted 10 October 2024; Issue published 06 December 2024

Abstract

The constantly increasing degree and frequency of cyber threats require the emergence of flexible and intelligent approaches to systems’ protection. Despite the calls for the use of artificial intelligence (AI) and machine learning (ML) in strengthening cyber security, there needs to be more literature on an integrated view of the application areas, open issues or trends in AI and ML for cyber security. Based on 90 studies, in the following literature review, the author categorizes and systematically analyzes the current research field to fill this gap. The review evidences that, in contrast to rigid rule-based systems that are static and specific to a given type of threat, AI and ML are more portable and effective in large-scale anomaly detection, malware classification, and prevention of phishing attacks by analyzing the data, learning the patterns, and improving the performance based on new data. Further, the study outlines significant themes, such as data quality, integration, and bias with AI/ML models, and underscores overcoming barriers to undertaking standard AI/ML integration. The contributions of this work are as follows: a thorough description of AI/ML applications in cyber security, discussions on the critical issues, and relevant opportunities and suggestions for future research. Consequently, the work contributes to establishing directions for creating and implementing AI/ML-based cyber security with demonstrable returns of technical solutions, organizational change, and ethicist interventions.

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Keywords

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