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Machine Learning for Detection of Cy...

Kalra, Geet.

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Machine Learning for Detection of Cyberattacks on Industrial Control Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning for Detection of Cyberattacks on Industrial Control Systems./
Author:	Kalra, Geet.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	68 p.
Notes:	Source: Masters Abstracts International, Volume: 85-02.
Contained By:	Masters Abstracts International85-02.
Subject:	Industrial engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30672364
ISBN:	9798380097352

Machine Learning for Detection of Cyberattacks on Industrial Control Systems.
Kalra, Geet.

Machine Learning for Detection of Cyberattacks on Industrial Control Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 68 p.

Source: Masters Abstracts International, Volume: 85-02.

Thesis (M.S.)--Massachusetts Institute of Technology, 2023.

This item must not be sold to any third party vendors.

Senior executives for industrial systems are increasingly facing the need to reassess their cyber risk as cyberattacks are on a steep rise. This is because of the rapid digitalization of traditional industries, designed to work for decades at a time when security was not a priority. Simultaneously, the available tools to detect these attacks have also increased. This thesis aims to help researchers and industry leaders understand how to implement machine learning (ML) as an early detection tool for anomalies (cyberattacks being a subset of anomalies) in their processes. With learnings from an end-to-end implementation of some state-of-the-art machine learning models and a literature survey, this thesis highlights the critical focus areas for managers looking to implement ML tools. The thesis also helps managers to understand research metrics and converts them into business goals that would allow for better decision-making and resource allocation.

ISBN: 9798380097352Subjects--Topical Terms:

526216
Industrial engineering.

Machine Learning for Detection of Cyberattacks on Industrial Control Systems.
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502 $a Thesis (M.S.)--Massachusetts Institute of Technology, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Senior executives for industrial systems are increasingly facing the need to reassess their cyber risk as cyberattacks are on a steep rise. This is because of the rapid digitalization of traditional industries, designed to work for decades at a time when security was not a priority. Simultaneously, the available tools to detect these attacks have also increased. This thesis aims to help researchers and industry leaders understand how to implement machine learning (ML) as an early detection tool for anomalies (cyberattacks being a subset of anomalies) in their processes. With learnings from an end-to-end implementation of some state-of-the-art machine learning models and a literature survey, this thesis highlights the critical focus areas for managers looking to implement ML tools. The thesis also helps managers to understand research metrics and converts them into business goals that would allow for better decision-making and resource allocation.
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