Neural network architecture to detect system faults / cyberattacks anomalies within a photovoltaic system connected to the grid

Gaggero, Giovanni Battista; Rossi, Mansueto; Girdinio, P.; Marchese, Mario

doi:10.1109/isaect47714.2019.9069683

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…An approach based on a fully-connected neural network autoencoder to detect cyberattacks within a photovoltaic system, similarly to the scheme proposed in this paper for storage systems, has been suggested by the same authors in Refs. [ 22 , 23 ].…”

Section: State Of the Artmentioning

confidence: 99%

Detecting Cyberattacks on Electrical Storage Systems through Neural Network Based Anomaly Detection Algorithm

Gaggero

Caviglia

Armellin

et al. 2022

Sensors

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Distributed Energy Resources (DERs) are growing in importance Power Systems. Battery Electrical Storage Systems (BESS) represent fundamental tools in order to balance the unpredictable power production of some Renewable Energy Sources (RES). Nevertheless, BESS are usually remotely controlled by SCADA systems, so they are prone to cyberattacks. This paper analyzes the vulnerabilities of BESS and proposes an anomaly detection algorithm that, by observing the physical behavior of the system, aims to promptly detect dangerous working conditions by exploiting the capabilities of a particular neural network architecture called the autoencoder. The results show the performance of the proposed approach with respect to the traditional One Class Support Vector Machine algorithm.

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Section: State Of the Artmentioning

confidence: 99%

Detecting Cyberattacks on Electrical Storage Systems through Neural Network Based Anomaly Detection Algorithm

Gaggero

Caviglia

Armellin

et al. 2022

Sensors

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“…Nowadays, data analytics is at the forefront of the war against cyber-attacks. Cybersecurity experts have been utilizing data analytics not only to improve the cybersecurity monitoring levels over their network streams but also to increase real-time detection of threat patterns and to conduct surveillance of real-time network streams [12], [13], [14]. Both supervised learning and unsupervised learning techniques in data analytics have been used in the detection process of malicious attacks [12], [15].…”

Section: Introductionmentioning

confidence: 99%

A novel fully convolutional neural network approach for detection and classification of attacks on industrial IoT devices in smart manufacturing systems

Shahin

Chen

Bouzary

et al. 2022

Int J Adv Manuf Technol

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Recently, Internet of things (IoT) devices have been widely implemented and technologically advanced in manufacturing settings to monitor, collect, exchange, analyze, and deliver data. However, this transition has increased the risk of cyberattacks, exponentially. Subsequently, developing effective intrusion detection systems based on deep learning algorithms has proven to become a reliable intelligence tool to protect Industrial IoT devices against cyber threats. This paper presents the implementation of two different classi cations and detection utilizing the Long Short-Term Memory (LSTM) architecture to address cyber-security concerns on three benchmark Industrial IoT datasets (BoT-IoT, UNSW-NB15, and TON-IoT) which take advantage of various deep learning algorithms. An overall analysis of the performance of the proposed models is provided. Augmenting the LSTM with Convolutional Neural Network (CNN) and Fully Convolutional Neural Network (FCN) achieves state-of-the-art performance in detecting cybersecurity threats.

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“…Terzi, Terzi & Sagiroglu [13] have used an unsupervised anomaly detection approach and Principal Component Analysis (PCA) to identify anomalies in public big network data to understand network behavior to distinguish cyber-attacks and to provide better detection in the future. Autoencoder has been used with dimension reduction to detect cyber-attack anomalies [14]. In another study, Wan et al [15] showed that using Wavelet Neural Network (WNN) to detect anomalies in industrial control communication systems can lead to better accuracy compared to using Back Propagation Neural Network (BPNN) in addition to being more adequate in real-time analysis.…”

Section: Introductionmentioning

confidence: 99%

Classification and Detection of Malicious Attacks in Industrial IoT Devices via Machine Learning

Shahin

Chen

Bouzary

et al. 2022

Lecture Notes in Mechanical Engineering

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The term “the Industrial Internet of Things” has become increasingly more pervasive in the context of manufacturing as digitization has become a business priority for many manufacturers. IIoT refers to a network of interconnected industrial devices, resulting in systems that can monitor, collect, exchange, analyze, and deliver valuable data and new insights. These insights can then help drive smarter, and faster business decisions for manufacturers. However, these benefits have come at the cost of creating a new attack vector for the malicious agents that aim at stealing manufacturing trade secrets, blueprints, or designs. As a result, cybersecurity concerns have become more relevant across the field of manufacturing. One of the main tracks of research in this field deals with developing effective cyber-security mechanisms and frameworks that can identify, classify, and detect malicious attacks in industrial IoT devices. In this paper, we have developed and implemented a classification and detection framework for addressing cyber-security concerns in industrial IoT which takes advantage of various machine learning algorithms. The results prove the satisfactory performance and robustness of the approach in classifying and detecting the attacks.

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Neural network architecture to detect system faults / cyberattacks anomalies within a photovoltaic system connected to the grid

Cited by 6 publications

References 16 publications

Detecting Cyberattacks on Electrical Storage Systems through Neural Network Based Anomaly Detection Algorithm

Detecting Cyberattacks on Electrical Storage Systems through Neural Network Based Anomaly Detection Algorithm

A novel fully convolutional neural network approach for detection and classification of attacks on industrial IoT devices in smart manufacturing systems

Classification and Detection of Malicious Attacks in Industrial IoT Devices via Machine Learning

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