Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning

Elsisi, Mahmoud; Tran, Minh-Quang; Mahmoud, Karar; Mansour, Diaa‐Eldin A.; Lehtonen, Matti; Darwish, Mohamed M. F.

doi:10.1109/access.2021.3083499

Cited by 80 publications

(43 citation statements)

References 46 publications

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“…Recently, machine learning approaches have been applied to different engineering applications [34][35][36][37], which in turn have been widely used for bearing diagnosis of induction machines. These methods can reduce the memory computation while remaining high classification accuracy.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment

et al. 2021

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In recent years, the internet of things (IoT) represents the main core of Industry 4.0 for cyberphysic systems (CPS) in order to improve the industrial environment. Accordingly, the application of IoT and CPS has been expanded in applied electrical systems and machines. However, cybersecurity represents the main challenge of the implementation of IoT against cyber-attacks. In this regard, this paper proposes a new IoT architecture based on utilizing machine learning techniques to suppress cyber-attacks for providing reliable and secure online monitoring for the induction motor status. In particular, advanced machine learning techniques are utilized here to detect cyber-attacks and motor status with high accuracy. The proposed infrastructure validates the motor status via communication channels and the internet connection with economical cost and less effort on connecting various networks. For this purpose, the CONTACT Element platform for IoT is adopted to visualize the processed data based on machine learning techniques through a graphical dashboard. Once the cyber-attacks signal has been detected, the proposed IoT platform based on machine learning will be visualized automatically as fake data on the dashboard of the IoT platform. Different experimental scenarios with data acquisition are carried out to emphasize the performance of the suggested IoT topology. The results confirm that the proposed IoT architecture based on the machine learning technique can effectively visualize all faults of the motor status as well as the cyber-attacks on the networks. Moreover, all faults of the motor status and the fake data, due to the cyber-attacks, are successfully recognized and visualized on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization, thereby contributing to enhancing the decision-making about the motor status. Furthermore, the introduced IoT architecture with Random Forest algorithm provides an effective detection for the faults on motor due to the vibration under industrial conditions with excellent accuracy of 99.03% that is significantly greater than the other machine learning algorithms. Besides, the proposed IoT has low latency to recognize the motor faults and cyber-attacks to present them in the main dashboard of the IoT platform.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment

et al. 2021

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“…Consequently, DC microgrids have been proven as one of the most efficient and cost-effective systems in the integration of RES with loads, as they decrease the AC-DC and DC-AC power conversion stages compared to AC microgrids [10,11]. Machine learning and artificial intelligence have shown promising performance in different electrical engineering applications [12][13][14][15][16] as well as power system components, e.g., power transformers and high voltage transmission lines [17][18][19][20][21][22][23]. Figure 1 illustrates the microgrid components in which the load and the diesel generator along with the wind turbines are connected to the AC side.…”

Section: Introductionmentioning

confidence: 99%

Novel Control Strategy for Enhancing Microgrid Operation Connected to Photovoltaic Generation and Energy Storage Systems

et al. 2021

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Recently, the penetration of energy storage systems and photovoltaics has been significantly expanded worldwide. In this regard, this paper presents the enhanced operation and control of DC microgrid systems, which are based on photovoltaic modules, battery storage systems, and DC load. DC–DC and DC–AC converters are coordinated and controlled to achieve DC voltage stability in the microgrid. To achieve such an ambitious target, the system is widely operated in two different modes: stand-alone and grid-connected modes. The novel control strategy enables maximum power generation from the photovoltaic system across different techniques for operating the microgrid. Six different cases are simulated and analyzed using the MATLAB/Simulink platform while varying irradiance levels and consequently varying photovoltaic generation. The proposed system achieves voltage and power stability at different load demands. It is illustrated that the grid-tied mode of operation regulated by voltage source converter control offers more stability than the islanded mode. In general, the proposed battery converter control introduces a stable operation and regulated DC voltage but with few voltage spikes. The merit of the integrated DC microgrid with batteries is to attain further flexibility and reliability through balancing power demand and generation. The simulation results also show the system can operate properly in normal or abnormal cases, thanks to the proposed control strategy, which can regulate the voltage stability of the DC bus in the microgrid with energy storage systems and photovoltaics.

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“…Especially, artificial machine learning techniques are used to do an excellent job of classification in various areas. Recently, many machine learning algorithms, such as random forest (RF), gradient boosting, support vector machines (SVM), k-nearest neighbors (kNN), and artificial neural networks (ANNs), have been adopted to identify various signals [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of Deep Learning Methods for an Intelligent Pathological Voice Detection System Using the Saarbruecken Voice Database

Lee

2021

Applied Sciences

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This work is focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for pathological voice detection using mel-frequency cepstral coefficients (MFCCs), linear prediction cepstrum coefficients (LPCCs), and higher-order statistics (HOSs) parameters. In total, 518 voice data samples were obtained from the publicly available Saarbruecken voice database (SVD), comprising recordings of 259 healthy and 259 pathological women and men, respectively, and using /a/, /i/, and /u/ vowels at normal pitch. Significant differences were observed between the normal and the pathological voice signals for normalized skewness (p = 0.000) and kurtosis (p = 0.000), except for normalized kurtosis (p = 0.051) that was estimated in the /u/ samples in women. These parameters are useful and meaningful for classifying pathological voice signals. The highest accuracy, 82.69%, was achieved by the CNN classifier with the LPCCs parameter in the /u/ vowel in men. The second-best performance, 80.77%, was obtained with a combination of the FNN classifier, MFCCs, and HOSs for the /i/ vowel samples in women. There was merit in combining the acoustic measures with HOS parameters for better characterization in terms of accuracy. The combination of various parameters and deep learning methods was also useful for distinguishing normal from pathological voices.

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Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning

Cited by 80 publications

References 46 publications

Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment

Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment

Novel Control Strategy for Enhancing Microgrid Operation Connected to Photovoltaic Generation and Energy Storage Systems

Experimental Evaluation of Deep Learning Methods for an Intelligent Pathological Voice Detection System Using the Saarbruecken Voice Database

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