Identification of Static and Dynamic Security Controls Using Machine Learning

Florencio Javier González-Rodriguez, Eleazar Aguirre-Anaya, Moises Salinas-Rosales, Atsuko Miyaji


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.


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

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