Machine learning methods for the industrial robotic systems security

Tsapin, Dmitry; Pitelinskiy, Kirill; Suvorov, Stanislav; Osipov, Aleksey; Pleshakova, Ekaterina; Gataullin, Sergey

doi:10.1007/s11416-023-00499-6

Cited by 4 publications

(2 citation statements)

References 48 publications

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“…Also, the most commonly used CNN architectures are MobileNetV2, ResNet50, and DenseNet121, which were applied by the authors [ 12 ] in a previous study. In this study, the authors achieved a 2–3% increase in accuracy rates, achieving 88% for MobileNetV2, 91% for ResNet50, and 92% for DenseNet121 by adding a squeeze-and-excitation (SE) block that allows the neural network to better extract important features.…”

Section: Related Workmentioning

confidence: 99%

Development of an Artificial Vision for a Parallel Manipulator Using Machine-to-Machine Technologies

Nussibaliyeva,

Sergazin,

Tursunbayeva

et al. 2024

Sensors

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This research focuses on developing an artificial vision system for a flexible delta robot manipulator and integrating it with machine-to-machine (M2M) communication to optimize real-time device interaction. This integration aims to increase the speed of the robotic system and improve its overall performance. The proposed combination of an artificial vision system with M2M communication can detect and recognize targets with high accuracy in real time within the limited space considered for positioning, further localization, and carrying out manufacturing processes such as assembly or sorting of parts. In this study, RGB images are used as input data for the MASK-R-CNN algorithm, and the results are processed according to the features of the delta robot arm prototype. The data obtained from MASK-R-CNN are adapted for use in the delta robot control system, considering its unique characteristics and positioning requirements. M2M technology enables the robot arm to react quickly to changes, such as moving objects or changes in their position, which is crucial for sorting and packing tasks. The system was tested under near real-world conditions to evaluate its performance and reliability.

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Section: Related Workmentioning

confidence: 99%

Development of an Artificial Vision for a Parallel Manipulator Using Machine-to-Machine Technologies

Nussibaliyeva,

Sergazin,

Tursunbayeva

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In [ 8 ], training of convolutional neural networks is conducted on the MobileNetV2, ResNet50, and DenseNet121 architectures, with the addition of a Squeeze-and-Excitation (SE) block also for visual information processing and robotic control. The capsular convolutional neural network (CapsNet) is analyzed in [ 9 ] for robotic control, where the authors developed two modifications of 1D-CapsNet and Windowed Fourier Transform (WFT)—2D-CapsNet.…”

Section: Introductionmentioning

confidence: 99%

A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks

Marrero,

Kern,

Urrea

2024

Sensors

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This paper investigates spiking neural networks (SNN) for novel robotic controllers with the aim of improving accuracy in trajectory tracking. By emulating the operation of the human brain through the incorporation of temporal coding mechanisms, SNN offer greater adaptability and efficiency in information processing, providing significant advantages in the representation of temporal information in robotic arm control compared to conventional neural networks. Exploring specific implementations of SNN in robot control, this study analyzes neuron models and learning mechanisms inherent to SNN. Based on the principles of the Neural Engineering Framework (NEF), a novel spiking PID controller is designed and simulated for a 3-DoF robotic arm using Nengo and MATLAB R2022b. The controller demonstrated good accuracy and efficiency in following designated trajectories, showing minimal deviations, overshoots, or oscillations. A thorough quantitative assessment, utilizing performance metrics like root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE), provides additional validation for the efficacy of the SNN-based controller. Competitive performance was observed, surpassing a fuzzy controller by 5% in terms of the ITAE index and a conventional PID controller by 6% in the ITAE index and 30% in RMSE performance. This work highlights the utility of NEF and SNN in developing effective robotic controllers, laying the groundwork for future research focused on SNN adaptability in dynamic environments and advanced robotic applications.

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Assessing the Effectiveness of Cyber Domain Controls When Conducting Cybersecurity Audits: Insights from Higher Education Institutions in Canada

Sabillon,

Higuera,

Cano

et al. 2024

Electronics

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This study validates a comprehensive cybersecurity audit model through empirical analysis in three higher education institutions in Canada. The research aims to enhance cybersecurity resilience by assessing the effectiveness of cybersecurity controls across diverse educational environments. Given the increasing frequency and sophistication of cyberattacks targeting educational institutions, this research is essential to ensure the protection of sensitive academic and personal data. Data were collected through detailed audits involving system vulnerabilities, compliance with security policies, and incident response management at each institution. The findings underscore the importance of tailored cybersecurity strategies and continuous auditing to mitigate cyber risks in the Canadian higher education sector. This study contributes to the field by validating a versatile audit tool that can be adapted to various institutional contexts, promoting enhanced cybersecurity practices and evaluating the effectiveness of cybersecurity safeguards across the higher education sector in Canada. The results of the audit model validations provide the cybersecurity maturity rating of each institution. Further research is recommended to refine the model and explore its application in other industries and sectors.

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Machine learning methods for the industrial robotic systems security

Cited by 4 publications

References 48 publications

Development of an Artificial Vision for a Parallel Manipulator Using Machine-to-Machine Technologies

Development of an Artificial Vision for a Parallel Manipulator Using Machine-to-Machine Technologies

A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks

Assessing the Effectiveness of Cyber Domain Controls When Conducting Cybersecurity Audits: Insights from Higher Education Institutions in Canada

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