Protecting Water Infrastructure from Cyber and Physical Threats: Using Multimodal Data Fusion and Adaptive Deep Learning to Monitor Critical Systems

Nikolaos Bakalos; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Avi Ostfeld; Elad Salomons; Juan Caubet; Victor Jimenez; Pau Li

doi:10.1109/MSP.2018.2885359

Protecting Water Infrastructure from Cyber and Physical Threats: Using Multimodal Data Fusion and Adaptive Deep Learning to Monitor Critical Systems

Nikolaos Bakalos, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Avi Ostfeld, Elad Salomons, Juan Caubet, Victor Jimenez, Pau Li

Research output: Contribution to journal › Article › peer-review

Abstract

Critical water infrastructure is susceptible to various types of major attacks, including direct, human-presence assaults and cyberattacks tampering with industrial control system (ICS) sensors and processes. As attacks become increasingly sophisticated and multifaceted, their timely detection becomes especially challenging and requires the exploitation of different data modalities, such as visual surveillance, channel state information (CSI) from Wi-Fi signals for human-presence detection, and ICS sensor data from the utility.

Original language	American English
Article number	8653521
Pages (from-to)	36-48
Number of pages	13
Journal	IEEE Signal Processing Magazine
Volume	36
Issue number	2
DOIs	https://doi.org/10.1109/MSP.2018.2885359
State	Published - Mar 2019
Externally published	Yes

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

UN SDGs

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

Access to Document

10.1109/MSP.2018.2885359

Cite this

Bakalos, N., Voulodimos, A., Doulamis, N., Doulamis, A., Ostfeld, A., Salomons, E., Caubet, J., Jimenez, V., & Li, P. (2019). Protecting Water Infrastructure from Cyber and Physical Threats: Using Multimodal Data Fusion and Adaptive Deep Learning to Monitor Critical Systems. IEEE Signal Processing Magazine, 36(2), 36-48. Article 8653521. https://doi.org/10.1109/MSP.2018.2885359

@article{e69f4ac9c510462da2224c7c56ef8635,

title = "Protecting Water Infrastructure from Cyber and Physical Threats: Using Multimodal Data Fusion and Adaptive Deep Learning to Monitor Critical Systems",

abstract = "Critical water infrastructure is susceptible to various types of major attacks, including direct, human-presence assaults and cyberattacks tampering with industrial control system (ICS) sensors and processes. As attacks become increasingly sophisticated and multifaceted, their timely detection becomes especially challenging and requires the exploitation of different data modalities, such as visual surveillance, channel state information (CSI) from Wi-Fi signals for human-presence detection, and ICS sensor data from the utility.",

author = "Nikolaos Bakalos and Athanasios Voulodimos and Nikolaos Doulamis and Anastasios Doulamis and Avi Ostfeld and Elad Salomons and Juan Caubet and Victor Jimenez and Pau Li",

note = "Publisher Copyright: {\textcopyright} 1991-2012 IEEE.",

year = "2019",

month = mar,

doi = "10.1109/MSP.2018.2885359",

language = "American English",

volume = "36",

pages = "36--48",

journal = "IEEE Signal Processing Magazine",

issn = "1053-5888",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "2",

}

Bakalos, N, Voulodimos, A, Doulamis, N, Doulamis, A, Ostfeld, A, Salomons, E, Caubet, J, Jimenez, V & Li, P 2019, 'Protecting Water Infrastructure from Cyber and Physical Threats: Using Multimodal Data Fusion and Adaptive Deep Learning to Monitor Critical Systems', IEEE Signal Processing Magazine, vol. 36, no. 2, 8653521, pp. 36-48. https://doi.org/10.1109/MSP.2018.2885359

TY - JOUR

T1 - Protecting Water Infrastructure from Cyber and Physical Threats

T2 - Using Multimodal Data Fusion and Adaptive Deep Learning to Monitor Critical Systems

AU - Bakalos, Nikolaos

AU - Voulodimos, Athanasios

AU - Doulamis, Nikolaos

AU - Doulamis, Anastasios

AU - Ostfeld, Avi

AU - Salomons, Elad

AU - Caubet, Juan

AU - Jimenez, Victor

AU - Li, Pau

PY - 2019/3

Y1 - 2019/3

N2 - Critical water infrastructure is susceptible to various types of major attacks, including direct, human-presence assaults and cyberattacks tampering with industrial control system (ICS) sensors and processes. As attacks become increasingly sophisticated and multifaceted, their timely detection becomes especially challenging and requires the exploitation of different data modalities, such as visual surveillance, channel state information (CSI) from Wi-Fi signals for human-presence detection, and ICS sensor data from the utility.

AB - Critical water infrastructure is susceptible to various types of major attacks, including direct, human-presence assaults and cyberattacks tampering with industrial control system (ICS) sensors and processes. As attacks become increasingly sophisticated and multifaceted, their timely detection becomes especially challenging and requires the exploitation of different data modalities, such as visual surveillance, channel state information (CSI) from Wi-Fi signals for human-presence detection, and ICS sensor data from the utility.

UR - http://www.scopus.com/inward/record.url?scp=85062818524&partnerID=8YFLogxK

U2 - 10.1109/MSP.2018.2885359

DO - 10.1109/MSP.2018.2885359

M3 - Article

SN - 1053-5888

VL - 36

SP - 36

EP - 48

JO - IEEE Signal Processing Magazine

JF - IEEE Signal Processing Magazine

IS - 2

M1 - 8653521

ER -

Protecting Water Infrastructure from Cyber and Physical Threats: Using Multimodal Data Fusion and Adaptive Deep Learning to Monitor Critical Systems

Abstract

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this