APPLICATION OF MACHINE LEARNING AND DEEP LEARNING TECHNIQUES FOR CYBER SECURITY
DOI:
https://doi.org/10.29121/shodhkosh.v5.i1.2024.3997Keywords:
Machine Learning, Intrusion Detection System, Deep Learning, CybersecurityAbstract [English]
Cybersecurity has become a fundamental necessity for all local and governmental organizations in today's digital landscape. Consequently, cybersecurity professionals have started to leverage machine learning and deep learning techniques to create and implement secure systems. So Cybersecurity has emerged as a crucial area of research. It focuses on three primary aspects: machine learning, deep learning, and cybersecurity itself. The review highlights important research on machine learning (ML) and deep learning (DL) techniques applied to intrusion detection within cybersecurity. It provides an in-depth analysis of the critical functions that ML and DL serve in this domain, encompassing both theoretical models and practical implementations.. Moreover, the paper establishes criteria for evaluating ML and DL methodologies and analyzes the complexities associated with different algorithms. Recognizing the critical role of data in these approaches, the paper highlights the datasets employed for analyzing network traffic and identifying anomalies. It emphasizes significant research on ML and DL techniques applied in network intrusion detection and offers a succinct overview of each method.
Additionally, this paper features a comparative analysis and explores how machine learning (ML) and deep learning (DL) are transforming cybersecurity, machine learning and deep learning algorithms employed in intrusion detection systems, focusing on learning algorithms, performance metrics, datasets, and specific attack scenarios. The applications of ML and DL in addressing cybersecurity threats are thoroughly discussed. Furthermore, it discusses the application encountered in the implementation of ML and DL in the cybersecurity field and suggests potential directions for future research.
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