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All Journal Bulletin of Electrical Engineering and Informatics Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems
Radhi, Ahmed Dheyaa
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

 1 
Towards Resilient Machine Learning Models: Addressing Adversarial Attacks in Wireless Sensor Network Shihab, Mustafa Abdmajeed; Marhoon, Haydar Abdulameer; Ahmed, Saadaldeen Rashid; Radhi, Ahmed Dheyaa; Sekhar, Ravi
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.23214

Abstract

Adversarial attacks represent a substantial threat to the security and reliability of machine learning models employed in wireless sensor networks (WSNs). This study tries to solve this difficulty by evaluating the efficiency of different defensive mechanisms in minimizing the effects of evasion assaults, which try to mislead ML models into misclassification. We employ the Edge-IIoTset dataset, a comprehensive cybersecurity dataset particularly built for IoT and IIoT applications, to train and assess our models. Our study reveals that employing adversarial training, robust optimization, and feature transformations dramatically enhances the resistance of machine learning models against evasion attempts. Specifically, our defensive model obtains a significant accuracy boost of 12% compared to baseline models. Furthermore, we study the possibilities of combining alternative generative adversarial networks (GANs), random forest ensembles, and hybrid techniques to further boost model resilience against a broader spectrum of adversarial assaults. This study underlines the need for proactive methods in preserving machine learning systems in real-world WSN contexts and stresses the need for continued research and development in this quickly expanding area.
Enhanced Dynamic Control of Quadcopter PMSMs Using an ILQR-PCC System for Improved Stability and Reduced Torque Ripples Saleh, Ziyaad H.; Mejbel, Basim Ghalib; Radhi, Ahmed Dheyaa; Hashim, Abdulghafor Mohammed; Taha, Taha A.; Gökşenli, Nurettin; Hussain, Abadal-Salam T.; Sekhar, Ravi
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23159

Abstract

Quadcopter technology has developed fast because it’s flexibility and capacity for high maneuvers. What makes PMSM suitable for Quadcopter is their high power to weight ratio, reliability and efficiency. These motors allow the operation of torque and speed control which are important for stability and maneuverability in the flight of the aircraft. Nevertheless, certain and smooth flight caused by regulation of PMSM speed and current is necessary for stable and maneuverable movement. This work presents a new control strategy connecting the ILQR control to govern the speed while the PCC profit the dynamic response and control torque ripples. A comparison is made on the performance of the ILQR-PCC system with nominal Proportional-Integral (PI) control and ILQR.  From the results it is evident that the ILQR-PCC system is far superior to both the PI ILQR controller in regards to the dynamic response, the disturbances rejection capacity as well as reducing the current signal distortions hence reducing the torque ripples. Its working was evidenced in a nonlinear LQR-controlled quadcopter to track the reference accurately and to have minimum distortion in current regulation. The presented work improves the control systems of quadcopters: it introduces a reliable method that improves stability and increases the performance of the quadcopter; therefore, this paper contributes to the existing knowledge.
Optimizing Network Security with Machine Learning and Multi-Factor Authentication for Enhanced Intrusion Detection Mahmood, Rafah Kareem; Mahameed, Ans Ibrahim; Lateef, Noor Q.; Jasim, Hasanain M.; Radhi, Ahmed Dheyaa; Ahmed, Saadaldeen Rashid; Tupe-Waghmare, Priyanka
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22508

Abstract

This study examines the utilization of machine learning methodologies and multi-factor authentication (MFA) to bolster network security, specifically targeting network intrusion detection. We analyze the way in which the integration of these technologies effectively tackles existing security concerns and constraints. The research highlights the importance of incorporating energy conservation and environmental impact reduction into security solutions, in addition to traditional cryptography and biometric methods. In addition, we tackle the limitations of centralized systems, such as vulnerabilities to security breaches and instances of system failures. The study examines different security models, encompassing categories, frameworks, consensus protocols, applications, services, and deployment goals in order to determine their impact on network security. In addition, we offer a detailed comparison of seven machine learning models, showcasing their effectiveness in enhancing network intrusion detection and overall security. The objective of this study is to provide in-depth understanding and actionable suggestions for utilizing machine learning with MFA (Multi-Factor Authentication) to enhance network defensive tactics.
Enhanced Total Harmonic Distortion Optimization in Cascaded H-Bridge Multilevel Inverters Using the Dwarf Mongoose Optimization Algorithm Salih, Sinan Q.; Mejbel, Basim Ghalib; Ahmad, B. A.; Taha, Taha A.; Bektaş, Yasin; Aldabbagh, Mohammed M; Hussain, Abadal-Salam T.; Hashim, Abdulghafor Mohammed; Radhi, Ahmed Dheyaa
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23548

Abstract

Total harmonic distortion (THD) is one of the most essential parameters that define the operational efficiency and power quality in electrical systems applied to solutions like cascaded H-bridge multilevel inverters (CHB-MLI). The reduction of THD is crucial due to the fact that improving the system’s power quality and minimizing the losses are key for performance improvement. The purpose of this work is to introduce a new DMO-based approach to optimize the THD of the output voltage in a three-phase nine-level CHB-MLI. The proposed DMO algorithm was also subjected to intense comparison with two benchmark optimization techniques, namely Genetic Algorithm and Particle Swarm Optimization with regards to three parameters, namely convergence rate, stability, and optimization accuracy. A series of MATLAB simulations were run to afford the evaluation of each algorithm under a modulation index of between 0.1 and 1.0. The outcome of the experiment amply proves that in comparison with THD minimization for the given OP, the DMO algorithm was significantly superior to both RSA-based GA and PSO algorithms in their ability to yield higher accuracy while requiring lesser computational time. Consequently, this work could expand the application of the DMO algorithm as a reliable and effective means of enhancing THD in CHB-MLIs as well as advancing the overall quality of power systems in different electrical power networks.
Optimizing Network Security with Machine Learning and Multi-Factor Authentication for Enhanced Intrusion Detection Mahmood, Rafah Kareem; Mahameed, Ans Ibrahim; Lateef, Noor Q.; Jasim, Hasanain M.; Radhi, Ahmed Dheyaa; Ahmed, Saadaldeen Rashid; Tupe-Waghmare, Priyanka
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22508

Abstract

This study examines the utilization of machine learning methodologies and multi-factor authentication (MFA) to bolster network security, specifically targeting network intrusion detection. We analyze the way in which the integration of these technologies effectively tackles existing security concerns and constraints. The research highlights the importance of incorporating energy conservation and environmental impact reduction into security solutions, in addition to traditional cryptography and biometric methods. In addition, we tackle the limitations of centralized systems, such as vulnerabilities to security breaches and instances of system failures. The study examines different security models, encompassing categories, frameworks, consensus protocols, applications, services, and deployment goals in order to determine their impact on network security. In addition, we offer a detailed comparison of seven machine learning models, showcasing their effectiveness in enhancing network intrusion detection and overall security. The objective of this study is to provide in-depth understanding and actionable suggestions for utilizing machine learning with MFA (Multi-Factor Authentication) to enhance network defensive tactics.
Enhanced Dynamic Control of Quadcopter PMSMs Using an ILQR-PCC System for Improved Stability and Reduced Torque Ripples Saleh, Ziyaad H.; Mejbel, Basim Ghalib; Radhi, Ahmed Dheyaa; Hashim, Abdulghafor Mohammed; Taha, Taha A.; Gökşenli, Nurettin; Hussain, Abadal-Salam T.; Sekhar, Ravi
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23159

Abstract

Quadcopter technology has developed fast because it’s flexibility and capacity for high maneuvers. What makes PMSM suitable for Quadcopter is their high power to weight ratio, reliability and efficiency. These motors allow the operation of torque and speed control which are important for stability and maneuverability in the flight of the aircraft. Nevertheless, certain and smooth flight caused by regulation of PMSM speed and current is necessary for stable and maneuverable movement. This work presents a new control strategy connecting the ILQR control to govern the speed while the PCC profit the dynamic response and control torque ripples. A comparison is made on the performance of the ILQR-PCC system with nominal Proportional-Integral (PI) control and ILQR.  From the results it is evident that the ILQR-PCC system is far superior to both the PI & ILQR controller in regards to the dynamic response, the disturbances rejection capacity as well as reducing the current signal distortions hence reducing the torque ripples. Its working was evidenced in a nonlinear LQR-controlled quadcopter to track the reference accurately and to have minimum distortion in current regulation. The presented work improves the control systems of quadcopters: it introduces a reliable method that improves stability and increases the performance of the quadcopter; therefore, this paper contributes to the existing knowledge.
Towards Resilient Machine Learning Models: Addressing Adversarial Attacks in Wireless Sensor Network Shihab, Mustafa Abdmajeed; Marhoon, Haydar Abdulameer; Ahmed, Saadaldeen Rashid; Radhi, Ahmed Dheyaa; Sekhar, Ravi
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.23214

Abstract

Adversarial attacks represent a substantial threat to the security and reliability of machine learning models employed in wireless sensor networks (WSNs). This study tries to solve this difficulty by evaluating the efficiency of different defensive mechanisms in minimizing the effects of evasion assaults, which try to mislead ML models into misclassification. We employ the Edge-IIoTset dataset, a comprehensive cybersecurity dataset particularly built for IoT and IIoT applications, to train and assess our models. Our study reveals that employing adversarial training, robust optimization, and feature transformations dramatically enhances the resistance of machine learning models against evasion attempts. Specifically, our defensive model obtains a significant accuracy boost of 12% compared to baseline models. Furthermore, we study the possibilities of combining alternative generative adversarial networks (GANs), random forest ensembles, and hybrid techniques to further boost model resilience against a broader spectrum of adversarial assaults. This study underlines the need for proactive methods in preserving machine learning systems in real-world WSN contexts and stresses the need for continued research and development in this quickly expanding area.
Co-Authors Abbas, Haider Hadi Abdul Wahab, Noor Izzri Ahmad, B. A. Ahmed, Ahmed H. Ahmed, Saadaldeen Rashid Ahmed, Shouket A. Al-Naimi, Taha Mahmoud Aldabbagh, Mohammed M Almalaisi, Taha Abdulsalam Almulaisi, Taha Assi, Saad A. Awad, Raad Hamza Azmi, Syahrul Ashikin binti Bektaş, Enes BEKTAS, YASIN Çiçek, Adem Desa, Hazry El-Khazali, Reyad Fadhil, Hilal A. Gökşenli, Nurettin Hashim, Abdulghafor Mohammed Hassan, Mohd Khair Hussain, Abadal-Salam T. Hussein, Shamil H. Imran, Ahmed Imad Itankar, Nilisha Jasim, Hasanain M. JosephNg, Poh Soon Khalaf, Ahmed Lateef Lateef, Noor Q. Mahameed, Ans Ibrahim Mahmood, Abdulkareem Nasir Mahmood, Rafah Kareem Majdi, Hasan S. Marhoon, Haydar Abdulameer Mejbel, Basim Ghalib Saleh, Ziyaad H. Salih, Sinan Q. Sekhar, Ravi Shihab, Mustafa Abdmajeed Solke, Nitin Taha, Faris Hassan Taha, Taha A. Tawfeq, Jamal Fadhil Tupe-Waghmare, Priyanka Yaseen, Mohammed T. Yassin, Khalil Farhan Zaynal, Hussein I.