A Taxonomy for Contrasting Industrial Control Systems Asset Discovery Tools

Date

2022-11-24T17:33:43Z

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

Cranfield University

Department

Type

Poster

ISSN

Format

Free to read from

Citation

Samanis, Manolis (2022). A Taxonomy for Contrasting Industrial Control Systems Asset Discovery Tools. Cranfield Online Research Data (CORD). Poster. https://doi.org/10.17862/cranfield.rd.21618573.v1

Abstract

The number of tools for scanning industrial assets has grown considerably over the past decade. There is currently a plethora of free and commercial asset scanning tools which specialize in industrial control system (ICS) devices. However, to the best of our knowledge, there is no information pertaining to their actual capabilities and no experimental comparative comparison of their features. Moreover, it is not clear to what depth of scanning these tools can reach and whether the tools are suitable to use in a scaled industrial network architecture. This poster provides the first systematic features comparison available on free to use asset scanning tools, on the basis of an ICS scanning taxonomy we propose. Based on the taxonomy, we investigate tools scanning depth and validate results through experimentation on Siemens and Allen Bradley devices.

Description

Software Description

Software Language

Github

Keywords

Asset scanning, Industrial control systems, SCADA, DSDS22, DSDS22 Poster

DOI

10.17862/cranfield.rd.21618573.v1

Rights

CC BY 4.0

Relationships

Relationships

Supplements

Funder/s

Airbus

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