박상범, 김휘강, 윤경찬, 안명식,

Vol. 34, No. 6, pp. 1211-1229, 12월. 2024
10.13089/JKIISC.2024.34.6.1211, Full Text:
Keywords: OS Fingerprinting, Network monitoring, Machine Learning, Deep Learning
Abstract

In this paper, we investigate the latest advancements in operating system (OS) identification, with a particular emphasis on fingerprinting techniques that leverage machine learning technology. Within the contemporary information security landscape, the importance of OS identification is becoming increasingly pronounced, particularly for tasks like asset management and vulnerability assessment. Recent research has witnessed significant efforts to address the limitations of fingerprinting through the integration of machine learning. Researches are actively exploring various data sources and algorithms to effectively distinguish between different operating systems. This paper provides a comprehensive review of the latest research trends in machine learning-based OS fingerprinting, focusing on how these approaches aim to overcome existing limitations of traditional methods. Additionally, the paper discusses the applicability of these techniques and explores potential future research directions within the field of OS identification.

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