ISSN:1005-3026

CYBERSECURITY ENHANCEMENT USING ADVANCED MACHINE LEARNING METHODS: A REVIEW

Ranjeeta Pandhare1, Dr. Jaydeep B. Patil2, Dr. Sangram T. Patil3

1Research Scholar, Department of Computer Science & Engineering, D. Y. Patil Agriculture and Technical University, Talsande, Maharashtra, India. Email: ranjeeta.pandhare@gmail.com

1Assistant Professor, Department of Computer Science & Engineering, Kolhapur Institute of Technology’s College of Engineering, Kolhapur, Maharashtra, India

2Associate Professor, Department of Computer Science & Engineering, D. Y. Patil Agriculture & Technical University, Talsande, Maharashtra, India. Email: jaydeeppatil@dyp-atu.org

3Associate Dean, Department of Computer Science & Engineering, D. Y. Patil Agriculture & Technical University, Talsande, Maharashtra, India.  Email: sangrampatil@dyp-atu.org

Abstract

In the digital age, cybersecurity has grown to be a major worry due to the growing sophistication of cyber-attacks that pose serious problems for both individuals and enterprises globally. Even while they can be somewhat effective, traditional cybersecurity techniques frequently fall behind the quickly changing threat landscape. Due to machine learning’s (ML) capacity to analyze massive amounts of data and spot anomalies or patterns indicative of dangerous behavior, it has emerged as a promising tool for improving cybersecurity measures. An extensive overview of the application of ML methods in cybersecurity is given in this study, covering various domains such as intrusion detection, malware analysis, phishing detection, and threat intelligence. We explore different ML algorithms such as supervised, supervised, and semi-supervised learning and highlight their strengths and limitations to address the cybersecurity challenges. Additionally, we go over how crucial feature selection, data preprocessing, and model validation are to creating successful machine learning cybersecurity systems. Additionally, we examine the integration of ML with other cybersecurity technologies such as cryptography and network security for enhanced protection against cyber threats. Lastly, we highlight the field’s present research trends and obstacles and offer recommendations for how machine learning might be used in the future to strengthen cybersecurity defences.

Keywords: Cybersecurity, Machine Learning, Intrusion Detection, Malware Analysis, Phishing Detection, Threat Intelligence.