Deep Learning Based Covert Attack Identification for Industrial Control Systems

Li, Dan; Ramanan, Paritosh; Gebraeel, Nagi; Paynabar, Kamran

doi:10.1109/icmla51294.2020.00075

Cited by 20 publications

(16 citation statements)

References 23 publications

(23 reference statements)

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“…Li et al [ 14 ] analyzed the sensor data of the wind turbine with professional knowledge and, after feature extraction, uses the hyperparameter search method to train the support vector machine model to diagnose faults. Finally, Yokouchi and Kondo [ 15 ] introduced a single-class SVM model to learn the boundary of the common data space by collecting the equipment's average data and applying it to the fault detection of the equipment.…”

Section: Related Workmentioning

confidence: 99%

Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment

Soni

Khan

Baseer

et al. 2022

Computational Intelligence and Neuroscience

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Industry 4.0, with the widespread use of IoT, is a significant opportunity to improve the reliability of industrial equipment through problem detection. It is difficult to utilize a unified model to depict the working condition of devices in real-world industrial scenarios because of the complex and dynamic relationship between devices. The scope of this research is that it can detect equipment defects and deploys them in a natural production environment. The proposed research is describing an online detection method for system failures based on long short-term memory neural networks. In recent years, deep learning technology has taken over as the primary method for detecting faults. A neural network with a long short-term memory is used to develop an online defect detection model. Feature extraction from sensor data is done using the curve alignment method. Based on long-term memory neural networks, the fault detection model is built (LSTM). In the end, sliding window technology is used to identify and fix the problem: the model’s online detection and update. The method’s efficacy is demonstrated by experiments based on real data from power plant sensors.

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Section: Related Workmentioning

confidence: 99%

Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment

Soni

Khan

Baseer

et al. 2022

Computational Intelligence and Neuroscience

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“…From the loss versus epoch variation, it worth saying that the convergence of the model was reached in the first ten iterations, which may lead to serious overfitting problems [113]. In [114], a cybersecurity diagnosis and localization method using hybridization of AE, RNN, LSTM, and DNN has been proposed. Despite the model's universality potential to cope with other networked industrial control systems, it has been found that this hybrid design can not detect unknown attack/fault types [114].…”

Section: H Hybrid Models-basedmentioning

confidence: 99%

“…In [114], a cybersecurity diagnosis and localization method using hybridization of AE, RNN, LSTM, and DNN has been proposed. Despite the model's universality potential to cope with other networked industrial control systems, it has been found that this hybrid design can not detect unknown attack/fault types [114]. A novel Graph Neural Network (GNN) based framework, combining graph convolutional network (GCN) and LSTM model has been proposed for multi-task multi-task transient stability classification [115].…”

Section: H Hybrid Models-basedmentioning

confidence: 99%

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

et al. 2021

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The current electric power system witnesses a significant transition into Smart Grids (SG) as a promising landscape for high grid reliability and efficient energy management. This ongoing transition undergoes rapid changes, requiring a plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. Motivated by the outstanding success of DL-based prediction methods, this article attempts to provide a thorough review from a broad perspective on the state-of-the-art advances of DL in SG systems. Firstly, a bibliometric analysis has been conducted to categorize this review's methodology. Further, we taxonomically delve into the mechanism behind some of the trending DL algorithms. We then showcase the DL enabling technologies in SG, such as federated learning, edge intelligence, and distributed computing. Finally, challenges and research frontiers are provided to serve as guidelines for future work in the futuristic power grid domain. This study's core objective is to foster the synergy between these two fields for decision-makers and researchers to accelerate DL's practical deployment for SG systems. INDEX TERMSSmart grid, deep learning, deep neural networks, edge computing, distributed and federated learning, power systems. NOMENCLATURE Abbreviations DDL Distributed deep learning DL Deep learning DRL Deep reinforcement learning DRN Deep residual network EI Edge intelligence EPS Electric power systems FL Federated learning IoT Internet of things LSTM Long short-term memory neural network The associate editor coordinating the review of this manuscript and approving it for publication was Shadi Alawneh . NN Neural network PVPF Photovoltaic power forecasting D. RESEARCH METHODOLOGY AND SYSTEMATIC REVIEW PROTOCOLStarting from September 2019, the multiple-methods approach was conducted [24]. The collection of the mainstream research papers on SG/AI from Web of Science (WoS), Scopus, IEEE Xplore, Science Direct, and Google scholar was conducted as the largest databases of peerreviewed articles. Only peer-reviewed articles written in English, providing experimental results, and having a unique identifier from the mentioned databases were taken into consideration, including reviews, research articles, patent reports, and conference proceedings. The adopted methodology for conducting this review article employs a combination of keywords categorized into three main groups, specifically, 'Deep Learning', 'Smart Grid', and 'Prediction'. The search methodology focuses on the recent research articles from 2015-2020 to identify the comprehensive statues of the AI applications on SG. The filtering process results in 220 research papers from 600 related papers selected based on their relevance by reading the title, abstract, conclusion,

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“…Furthermore, the proposed wireless interference level estimation is a general approach that can be applied in different industrial use cases. The approach follows the idea of using a data-driven anomaly detection and estimation approach in industrial control systems [11]. In this approach, a system model is trained for the industrial use case, while performing 5 ______________________________________________________________________________________________________ under different conditions, using a machine learning technique.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based wireless interference estimation in a robotic force-seeking application

Candell¹,

Kashef²,

Montgomery³

et al. 2022

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Wide deployment of wireless communications plays an essential role in the vision of future cyber-physical systems (CPSs), which includes massive transfer of automation information. Many practical considerations of industrial CPSs affect the success of the deployment of industrial wireless networks, and, thus, can inhibit their widespread adoption. These considerations include multi-path propagation, network congestion, and jamming interference. Jamming is of chief concern when wireless is used for mission critical or safety integrated systems. In this paper, an experimental platform consisting of a robot arm depressing a spring mechanism with a wireless force-feedback control algorithm is constructed. The robot applies downward pressure on a spring assembly until a predetermined force is detected and transmitted successfully to the controller under varying levels of sustained interference. Machine learning is used to learn and predict the signal-to-interference level of the communication link solely using position information from an independent vision tracking system. Various supervised learning algorithms are investigated and rated according to their performance.

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Deep Learning Based Covert Attack Identification for Industrial Control Systems

Cited by 20 publications

References 23 publications

Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment

Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

Machine learning based wireless interference estimation in a robotic force-seeking application

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