Feature Classification for Control System Devices

M. Rayhan Ahmed Mithu; Mike Rogers; Denis Ulybyshev; Rajesh Manicavasagam; Rima Asmar Awad

doi:10.32473/flairs.v34i1.128626

Feature Classification for Control System Devices

M. Rayhan Ahmed Mithu, Mike Rogers, Denis Ulybyshev, Rajesh Manicavasagam, Rima Asmar Awad

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

Control systems are used to automate industrial processes, smart grids, and smart cities. Unfortunately, cyber attacks on control systems are on the rise. Additionally, control systems lack the plethora of tools available for commodity systems for forensic investigation. An important step towards the proper forensic investigation is to analyze device memory. To assist in identifying features of device memory, we present a machine learning-based technique that integrates ontology information for feature classification in a control system device’s memory.

Original language	English
Journal	Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS
Volume	34
DOIs	https://doi.org/10.32473/flairs.v34i1.128626
State	Published - 2021
Externally published	Yes
Event	34th International Florida Artificial Intelligence Research Society Conference, FLAIRS-34 2021 - North Miami Beach, United States Duration: May 16 2021 → May 19 2021

Funding

We thank Zishan Ahmed Onik and Caleb Huck for their help with the project implementation. We thank Anthony Palmer and Bradley Northern for their feedback. We also thank the Center for Energy Systems Research at Tennessee Technological University for equipment and funding support.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.32473/flairs.v34i1.128626

Cite this

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title = "Feature Classification for Control System Devices",

abstract = "Control systems are used to automate industrial processes, smart grids, and smart cities. Unfortunately, cyber attacks on control systems are on the rise. Additionally, control systems lack the plethora of tools available for commodity systems for forensic investigation. An important step towards the proper forensic investigation is to analyze device memory. To assist in identifying features of device memory, we present a machine learning-based technique that integrates ontology information for feature classification in a control system device{\textquoteright}s memory.",

author = "Mithu, {M. Rayhan Ahmed} and Mike Rogers and Denis Ulybyshev and Rajesh Manicavasagam and Awad, {Rima Asmar}",

note = "Publisher Copyright: {\textcopyright} 2021by the authors. All rights reserved.; 34th International Florida Artificial Intelligence Research Society Conference, FLAIRS-34 2021 ; Conference date: 16-05-2021 Through 19-05-2021",

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doi = "10.32473/flairs.v34i1.128626",

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T1 - Feature Classification for Control System Devices

AU - Mithu, M. Rayhan Ahmed

AU - Rogers, Mike

AU - Ulybyshev, Denis

AU - Manicavasagam, Rajesh

AU - Awad, Rima Asmar

PY - 2021

Y1 - 2021

N2 - Control systems are used to automate industrial processes, smart grids, and smart cities. Unfortunately, cyber attacks on control systems are on the rise. Additionally, control systems lack the plethora of tools available for commodity systems for forensic investigation. An important step towards the proper forensic investigation is to analyze device memory. To assist in identifying features of device memory, we present a machine learning-based technique that integrates ontology information for feature classification in a control system device’s memory.

AB - Control systems are used to automate industrial processes, smart grids, and smart cities. Unfortunately, cyber attacks on control systems are on the rise. Additionally, control systems lack the plethora of tools available for commodity systems for forensic investigation. An important step towards the proper forensic investigation is to analyze device memory. To assist in identifying features of device memory, we present a machine learning-based technique that integrates ontology information for feature classification in a control system device’s memory.

UR - http://www.scopus.com/inward/record.url?scp=85131120409&partnerID=8YFLogxK

U2 - 10.32473/flairs.v34i1.128626

DO - 10.32473/flairs.v34i1.128626

M3 - Conference article

AN - SCOPUS:85131120409

SN - 2334-0754

VL - 34

JO - Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS

JF - Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS

T2 - 34th International Florida Artificial Intelligence Research Society Conference, FLAIRS-34 2021

Y2 - 16 May 2021 through 19 May 2021

ER -

Feature Classification for Control System Devices

Abstract

Funding

UN SDGs

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