Abstract:

With the rapid evolution of the Internet, cyber-attacks are becoming increasingly sophisticated, making cybersecurity a growing concern. This survey report presents a comprehensive review of key literature on machine learning (ML) and deep learning (DL) techniques for network intrusion detection. It provides a concise tutorial on various ML/DL methods, summarizing relevant research papers based on their temporal or thermal correlations. Given the critical role of data in ML/DL-based approaches, we highlight commonly used network datasets, discuss the challenges associated with applying ML/DL in cybersecurity, and suggest future research directions.

Description:

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