Abstract

GOMEZ CASTANO, Julio César; CASTANO PEREZ, Néstor Jaime  and  CORREA ORTIZ, Luis Carlos. Intrusion Detection and Prevention Systems: an Open Source Based Experimental Taxonomy Oriented to Industry 4.0. Cienc. Ing. Neogranad. [online]. 2023, vol.33, n.1, pp.75-86.  Epub June 30, 2023. ISSN 0124-8170.  https://doi.org/10.18359/rcin.6534.

this paper presents a proposed open source-based experimental taxonomy for an Intrusion Detection System/Intrusion Prevention System (IDS/IPS) oriented to Industry 4.0 due to the current information security needs in homes and enterprises. With the digital transformation, the exponential growth of the Internet of Things (IOT), Internet connections, and the increase of threats, the security problems of the equipment increase, which can be vulnerable to cybercriminals and be used as an intermediary to attack other equipment of the own network, of other organizations or to form their botnet with a view to massive controlled attacks. Therefore, necessary to have IDS/IPS to help improve their security. The taxonomy describes the technological infrastructure in hardware and software to arrange in an experimental environment and perform tests in the implementation, administration, management, and research of open source IDS/IPS and understand the rules and anomalies for intrusion detection through the signature database and the use of machine learning algorithms.

Keywords : IDS; IPS; open source; IoT; Machine Learning.

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