Diaz-Honrubia, Antonio Jesus, Herranz, Alberto Blázquez, Santamaría, Lucía Prieto, Ruiz, Ernestina Menasalvas, Rodríguez-González, Alejandro, Gonzalez-Granadillo, Gustavo, Diaz, Rodrigo, Panaousis, Emmanouil ORCID: https://orcid.org/0000-0001-7306-4062 and Xenakis, Christos (2022) A trusted platform module-based, pre-emptive and dynamic asset discovery tool. Journal of Information Security and Applications, 71:103350. ISSN 2214-2126 (Online) (doi:10.1016/j.jisa.2022.103350)

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Abstract

This paper presents an original Intelligent and Secure Asset Discovery Tool (ISADT) that uses artificial intelligence and TPM-based technologies to: (i) detect the network assets, and (ii) detect suspicious pattern in the use of the network. The architecture has specifically been designed to discover the assets of medium and large size companies and institutions, such as hospitals, universities, or government buildings. Given the distributed design of the architecture, it can cope with the problem of the isolation of different Virtual Local Area Networks (VLANs). This is done by collecting information from all the VLANs and storing it in a central node, which can be accessed by the network administrator, who may consult and visualize the status in any moment, or even by other authorized applications. The collected data is kept in a secure warehouse by the use of a Trusted Platform Module. Moreover, collected data is processed by the use of artificial intelligence in two ways: (i) the traffic of each network is analysed so that suspicious patterns can be detected, and (ii) identified ports and status are analysed to detect anomalous combinations of open ports in a device.

Item Type:	Article
Uncontrolled Keywords:	asset discovery; cyber security; network visualization; artificial intelligence; trusted platform module
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > Internet of Things and Security Research Centre (ISEC) Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	21 Oct 2023 01:38
URI:	http://gala.gre.ac.uk/id/eprint/37814

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