Machine learning and user interface for cyber risk management of water infrastructure
© 2023 Society for Risk Analysis..
With the continuous modernization of water plants, the risk of cyberattacks on them potentially endangers public health and the economic efficiency of water treatment and distribution. This article signifies the importance of developing improved techniques to support cyber risk management for critical water infrastructure, given an evolving threat environment. In particular, we propose a method that uniquely combines machine learning, the theory of belief functions, operational performance metrics, and dynamic visualization to provide the required granularity for attack inference, localization, and impact estimation. We illustrate how the focus on visual domain-aware anomaly exploration leads to performance improvement, more precise anomaly localization, and effective risk prioritization. Proposed elements of the method can be used independently, supporting the exploration of various anomaly detection methods. It thus can facilitate the effective management of operational risk by providing rich context information and bridging the interpretation gap.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:44 |
|---|---|
| Enthalten in: |
Risk analysis : an official publication of the Society for Risk Analysis - 44(2024), 4 vom: 25. Apr., Seite 833-849 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Neshenko, Nataliia [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Anomaly detection |
|---|
| Anmerkungen: |
Date Revised 05.04.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1111/risa.14209 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM361332483 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM361332483 | ||
| 003 | DE-627 | ||
| 005 | 20240405233111.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1111/risa.14209 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1366.xml |
| 035 | |a (DE-627)NLM361332483 | ||
| 035 | |a (NLM)37635130 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Neshenko, Nataliia |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Machine learning and user interface for cyber risk management of water infrastructure |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 05.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2023 Society for Risk Analysis. | ||
| 520 | |a With the continuous modernization of water plants, the risk of cyberattacks on them potentially endangers public health and the economic efficiency of water treatment and distribution. This article signifies the importance of developing improved techniques to support cyber risk management for critical water infrastructure, given an evolving threat environment. In particular, we propose a method that uniquely combines machine learning, the theory of belief functions, operational performance metrics, and dynamic visualization to provide the required granularity for attack inference, localization, and impact estimation. We illustrate how the focus on visual domain-aware anomaly exploration leads to performance improvement, more precise anomaly localization, and effective risk prioritization. Proposed elements of the method can be used independently, supporting the exploration of various anomaly detection methods. It thus can facilitate the effective management of operational risk by providing rich context information and bridging the interpretation gap | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a ICS cybersecurity | |
| 650 | 4 | |a anomaly detection | |
| 650 | 4 | |a operational risk | |
| 650 | 4 | |a visual analytics | |
| 650 | 4 | |a water treatment plants | |
| 700 | 1 | |a Bou-Harb, Elias |e verfasserin |4 aut | |
| 700 | 1 | |a Furht, Borko |e verfasserin |4 aut | |
| 700 | 1 | |a Behara, Ravi |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Risk analysis : an official publication of the Society for Risk Analysis |d 1990 |g 44(2024), 4 vom: 25. Apr., Seite 833-849 |w (DE-627)NLM012965340 |x 1539-6924 |7 nnns |
| 773 | 1 | 8 | |g volume:44 |g year:2024 |g number:4 |g day:25 |g month:04 |g pages:833-849 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1111/risa.14209 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 44 |j 2024 |e 4 |b 25 |c 04 |h 833-849 | ||