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

Bakalos, Nikolaos; Voulodimos, Athanasios; Doulamis, Nikolaos; Ostfeld, Avi; Salomons, Elad; Caubet, Juan; Jiménez, Víctor; Li, Pau

doi:10.1109/msp.2018.2885359

Cited by 59 publications

(40 citation statements)

References 27 publications

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“…Bakalos et al [95] developed a cyber-attack detection approach for water systems using multimodal data fusion and adaptive deep learning. Multimodal data fusion involves combining different channels of information, including visual data from thermal camera streams, Wi-Fi reflection, and ICS data.…”

Section: Cyber-attack Detection Modelsmentioning

confidence: 99%

A Systematic Review of the State of Cyber-Security in Water Systems

Tuptuk

Hazell

Watson

et al. 2021

Water

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Critical infrastructure systems are evolving from isolated bespoke systems to those that use general-purpose computing hosts, IoT sensors, edge computing, wireless networks and artificial intelligence. Although this move improves sensing and control capacity and gives better integration with business requirements, it also increases the scope for attack from malicious entities that intend to conduct industrial espionage and sabotage against these systems. In this paper, we review the state of the cyber-security research that is focused on improving the security of the water supply and wastewater collection and treatment systems that form part of the critical national infrastructure. We cover the publication statistics of the research in this area, the aspects of security being addressed, and future work required to achieve better cyber-security for water systems.

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Section: Cyber-attack Detection Modelsmentioning

confidence: 99%

A Systematic Review of the State of Cyber-Security in Water Systems

Tuptuk

Hazell

Watson

et al. 2021

Water

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“…A CNN can be used to estimate crowd density at railway stations [173],to detect intrusions in track areas, such as pedestrians or large livestock via images captured in railway areas [174], to monitor railway construction [152] and for intrusion detection at railway crossings [175]. From the security side, the method been used for detecting violent crowd flows [176], protect the critical infrastructure [177], and identifying tools wielding by attackers such as knives, guns and Explosives [178].…”

Section: Related Work In Railway Systemsmentioning

confidence: 99%

A Deep Learning Approach Towards Railway Safety Risk Assessment

Alawad

Kaewunruen

2020

IEEE Access

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Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks.

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“…Audebert [14] studied the use of a deep full convolutional neural network (DFCNN) in pixel-level scene markers of Earth observation images in the image field and achieved good experimental results. Bakalos et al [15] used multi-modal data fusion and adaptive deep learning to monitor critical water infrastructure and also gained valuable application results.…”

Section: Related Workmentioning

confidence: 99%

“…, w n }, we extract the word vector of the keyword t i as the attention matrix. The attention matrix s and the word vector matrix are subjected to the arithmetic operation shown in Equation (15), and the attention feature matrix A c can be obtained, wherein A c is a diagonal matrix. The operation process is shown in Figure 5:…”

Section: Word Vector Attention Mechanismmentioning

confidence: 99%

Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism

Zhang

Fang-ai

et al. 2019

Future Internet

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Convolutional neural networks (CNN) and long short-term memory (LSTM) have gained wide recognition in the field of natural language processing. However, due to the pre- and post-dependence of natural language structure, relying solely on CNN to implement text categorization will ignore the contextual meaning of words and bidirectional long short-term memory (BiLSTM). The feature fusion model is divided into a multiple attention (MATT) CNN model and a bi-directional gated recurrent unit (BiGRU) model. The CNN model inputs the word vector (word vector attention, part of speech attention, position attention) that has been labeled by the attention mechanism into our multi-attention mechanism CNN model. Obtaining the influence intensity of the target keyword on the sentiment polarity of the sentence, and forming the first dimension of the sentiment classification, the BiGRU model replaces the original BiLSTM and extracts the global semantic features of the sentence level to form the second dimension of sentiment classification. Then, using PCA to reduce the dimension of the two-dimensional fusion vector, we finally obtain a classification result combining two dimensions of keywords and sentences. The experimental results show that the proposed MATT-CNN+BiGRU fusion model has 5.94% and 11.01% higher classification accuracy on the MRD and SemEval2016 datasets, respectively, than the mainstream CNN+BiLSTM method.

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

Cited by 59 publications

References 27 publications

A Systematic Review of the State of Cyber-Security in Water Systems

A Systematic Review of the State of Cyber-Security in Water Systems

A Deep Learning Approach Towards Railway Safety Risk Assessment

Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism

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