CS Other Presentations

Department of Computer Science - University of Cyprus

Besides Colloquiums, the Department of Computer Science at the University of Cyprus also holds Other Presentations (Research Seminars, PhD Defenses, Short Term Courses, Demonstrations, etc.). These presentations are given by scientists who aim to present preliminary results of their research work and/or other technical material. Other Presentations serve as a forum for educating Computer Science students and related announcements are disseminated to the Department of Computer Science (i.e., the csall list):

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Presentations Coordinator: Demetris Zeinalipour

PhD Defense: A Framework for Blockchain-Based Data-Driven Fault Tolerant Control in the Industrial Internet of Things Enabled Smart Factories, Mr. Abdullah Bin Masood (University of Cyprus, Cyprus), Friday, February 23, 2024, 11.30-12.30 EET.

The Department of Computer Science at the University of Cyprus cordially invites you to the PhD Defense entitled:

A Framework for Blockchain-Based Data-Driven Fault Tolerant Control in the Industrial Internet of Things Enabled Smart Factories

Abstract:
Modern technologies and data-driven approaches have enabled fault-tolerant controllers in Industry 4.0 smart factories to detect, identify, and mitigate anomalies in real time with a high level of accuracy. However, this has also presented new challenges and requirements for cybersecurity, data analytics, and computational complexity for Data-Driven Fault-Tolerant Controllers (DD-FTC) in smart factories. To address these issues, a Blockchain-Based Data-Driven Fault-Tolerant Control (BB-DD-FTC) framework for smart factories is proposed in this thesis work. Blockchain ensures the integrity of data logs via its immutable ledger and decentralized architecture. Moreover, the blockchain smart contract functionality, embedded with a Data-Driven Intrusion Detection System (DD-IDS), and reconfiguration conditions, realizes DD-FTC and undertakes the mitigation response in case of cyber-attacks. The DD-IDS mechanism utilizes the principal component analysis technique and observer models, trained via neural networks, to detect an attack and identify the compromised component. The Tennessee Eastman (TE) industrial benchmark process is considered a case study to investigate the performance of the proposed framework. Two kinds of integrity attacks are applied to the sensors of the TE process with simulation results demonstrating the effectiveness of the method in mitigating the adversarial effect of the applied attacks on the overall system performance. As feedback delays can negatively impact performance, a detailed delay analysis is performed using network calculus. The security advantages and limitations of the proposed method are finally highlighted in the performed security analysis. The results are encouraging for the wider adoption of the control-over-the-blockchain concept.

Short Bio:
Abdullah Bin Masood is a Ph.D. candidate at the Department of Computer Science of the University of Cyprus under the supervision of Associate Professor Vasos Vassiliou. He has secured a PhD Doctoral Training Program (DTP) fellowship from CYENS – Centre of Excellence. His research interests lie in the areas of Blockchain, Anomaly Detection, and Deep Reinforcement Learning. He has a bachelor's in Electronics Engineering from GIKI in 2014 and a master’s in Electrical Engineering from the NUST, Pakistan in 2019.

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