Protecting Water Infrastructure From Cyber and Physical Threats Using multimodal data fusion and adaptive deep learning to monitor critical systems: 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: 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 › Editorial

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	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

UN SDGs

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

SDG 6 Clean Water and Sanitation
SDG 9 Industry, Innovation, and Infrastructure

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

Access to Document

10.1109/MSP.2018.2885359

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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: 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

Bakalos, Nikolaos ; Voulodimos, Athanasios ; Doulamis, Nikolaos et al. / Protecting Water Infrastructure From Cyber and Physical Threats Using multimodal data fusion and adaptive deep learning to monitor critical systems : Using Multimodal Data Fusion and Adaptive Deep Learning to Monitor Critical Systems. In: IEEE Signal Processing Magazine. 2019 ; Vol. 36, No. 2. pp. 36-48.

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title = "Protecting Water Infrastructure From Cyber and Physical Threats Using multimodal data fusion and adaptive deep learning to monitor critical systems: 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.",

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doi = "10.1109/MSP.2018.2885359",

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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: 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

Protecting Water Infrastructure From Cyber and Physical Threats Using multimodal data fusion and adaptive deep learning to monitor critical systems: Using Multimodal Data Fusion and Adaptive Deep Learning to Monitor Critical Systems. / Bakalos, Nikolaos; Voulodimos, Athanasios; Doulamis, Nikolaos et al.
In: IEEE Signal Processing Magazine, Vol. 36, No. 2, 8653521, 03.2019, p. 36-48.

Research output: Contribution to journal › Editorial

TY - JOUR

T1 - Protecting Water Infrastructure From Cyber and Physical Threats Using multimodal data fusion and adaptive deep learning to monitor critical systems

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.

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DO - 10.1109/MSP.2018.2885359

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SN - 1053-5888

VL - 36

SP - 36

EP - 48

JO - IEEE Signal Processing Magazine

JF - IEEE Signal Processing Magazine

IS - 2

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ER -

Bakalos N, Voulodimos A, Doulamis N, Doulamis A, Ostfeld A, Salomons E et al. Protecting Water Infrastructure From Cyber and Physical Threats Using multimodal data fusion and adaptive deep learning to monitor critical systems: Using Multimodal Data Fusion and Adaptive Deep Learning to Monitor Critical Systems. IEEE Signal Processing Magazine. 2019 Mar;36(2):36-48. 8653521. 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: Using Multimodal Data Fusion and Adaptive Deep Learning to Monitor Critical Systems

Abstract

UN SDGs

ASJC Scopus subject areas

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