Identification of Static and Dynamic Security Controls Using Machine Learning

Authors

  • Florencio Javier González-Rodriguez Instituto Politécnico Nacional Centro de Investigación en Computación del
  • Eleazar Aguirre-Anaya Instituto Politécnico Nacional Centro de Investigación en Computación del
  • Moises Salinas-Rosales Instituto Politécnico Nacional Centro de Investigación en Computación del
  • Atsuko Miyaji Osaka University

DOI:

https://doi.org/10.13053/cys-27-2-4429

Keywords:

OS obfuscation, OS fingerprinting, moving target defense identification, security architecture, machine learning

Abstract

During a network scanning, identifying the operating system (OS) running on each network attached host has been a research topic for a long time. Researchers have developed different approaches through network analysis using either passive or active techniques, such techniques are commonly called “OS fingerprinting”. According to best security practices, a set of security mechanisms should be applied to prevent OS fingerprinting by penetration testers. This paper proposes a strategy to identify obfuscation network devices during a black-box security assessment, using machine learning algorithms to offer a near approximation to the target architecture.

Author Biographies

Florencio Javier González-Rodriguez, Instituto Politécnico Nacional Centro de Investigación en Computación del

Ph.D. Student at Centro de Investigación en Computación del Instituto Politecnico Nacional

Eleazar Aguirre-Anaya, Instituto Politécnico Nacional Centro de Investigación en Computación del

Professor at Laboratory of Cybersecurity

Moises Salinas-Rosales, Instituto Politécnico Nacional Centro de Investigación en Computación del

Professor at Laboratory of Cybersecurity

Atsuko Miyaji, Osaka University

Professor, Department of Information and Communications Technology

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Published

2023-06-17

Issue

Vol. 27 No. 2 (2023): 27(2) 2023

Section

Articles

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