Seminar: Kommunikationssysteme

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Details

Type of course Seminar (2 SWS)
ECTS credits 5
Language English
Seminar Thursday, 12:15 am – 2:00 pm, room 04.137 campo
First meeting Thursday, October 26

A massive power grid transformation was needed by introducing renewable energy resources (like the Sun, Water, and Wind). As traditional power grids evolve into intelligent and highly interconnected systems, their reliance on digital technologies and communication networks intensifies significantly. Innovative concepts have been introduced to augment the intelligence of traditional power grids, including smart grids, microgrids, and smart meters. While this enhanced connectivity presents opportunities for improved efficiency and control, it also exposes the grid infrastructure to heightened cyber threats. Consequently, establishing robust cybersecurity measures has become imperative to safeguard against cyberattacks. Ensuring the reliable and secure operation of the smart grid plays a pivotal role in shaping the contemporary landscape of modern energy infrastructure.

In the context of this seminar on Cybersecurity in Smart Grids, our endeavor starts with the establishment of foundational principles underpinning the security of smart grids. Subsequently, we engage participants in interactive talk sessions meticulously designed to acquaint them with essential methodologies and best practices within this domain. By the culmination of this seminar, attendees will have acquired the necessary knowledge to delve into original research literature and proficiently deliver presentations on modern research topics concerning the cybersecurity aspects of smart grids. It is imperative to note that this seminar will be conducted in English. A prerequisite for participation is computer science comprehension and a fundamental understanding of machine learning, deep learning algorithms, and cybersecurity principles. While familiarity with the concepts covered in the lectures “Computer Networks” and “Communication Systems” is advantageous, it is not obligatory. Essential linear algebra concepts will suffice as a mathematics background. This seminar is open to bachelor’s and master’s students pursuing degrees in computer science, information, and communication technology, and related programs.

We firmly advocate that the study of Cybersecurity in Smart Grids holds paramount importance, particularly in light of the escalating convergence of networking technologies and concepts such as Artificial Intelligence (AI), and the Internet of Things (IoT). This seminar offers a comprehensive overview of cybersecurity in the context of smart grids. It is a direct pathway to explore potential research avenues presently being investigated within our laboratory. It is worth mentioning that our lab offers a new course entitled Cyber Security for Smart Grids, which is a great opportunity for students who want to steer their career toward this topic.

Introduction phase (by i7 personnel)
  • Introduction to Cyber Security for Smart Grids, based on [7] (2-3 weeks)
  • Interactive talk to get familiar with the topic, based on literature (1 week)
Topics of student talks (all are comprehensible with the previously provided knowledge)
  • Intrusion Detection for Cybersecurity of Smart Meters [1]
  • A Survey of Denial-of-Service Attacks and Solutions in the Smart Grid [2]
  • Flow-based monitoring of ICS communication in the smart grid [3]
  • Blockchain for Cybersecurity in Smart Grid [4]
  • Cyber security for SCADA systems [5,6]
  • Ensemble Learning Methods for Anomaly Intrusion Detection System in Smart Grid [8]
  • CNN for Detecting Cyber Attack of DDoS in Smart Grid [10]
  • Hybrid Deep Learning for Intrusion Detection in Smart Grid Networks [11]
  • This list can be extended on demand by additional topics of recent research

Student talks will be approximately 40 minutes and a documentation has to be written until the end of the semester.

  • [1] Sun, Chih-Che, D. Jonathan Sebastian Cardenas, Adam Hahn, and Chen-Ching Liu. “Intrusion detection for cybersecurity of smart meters.” IEEE Transactions on Smart Grid 12, no. 1 (2020): 612-622.
  • [2] Huseinović, Alvin, Saša Mrdović, Kemal Bicakci, and Suleyman Uludag. “A survey of denial-of-service attacks and solutions in the smart grid.” IEEE Access 8 (2020): 177447-177470.
  • [3] Matoušek, Petr, Ondřej Ryšavý, Matěj Grégr, and Vojtěch Havlena. “Flow-based monitoring of ICS communication in the smart grid.” Journal of Information Security and Applications 54 (2020): 102535.
  • [4] Zhuang, Peng, Talha Zamir, and Hao Liang. “Blockchain for cybersecurity in smart grid: A comprehensive survey.” IEEE Transactions on Industrial Informatics 17, no. 1 (2020): 3-19.
  • [5] Ferrag, Mohamed Amine, Messaoud Babaghayou, and Mehmet Akif Yazici. “Cyber security for fog-based smart grid SCADA systems: Solutions and challenges.” Journal of Information Security and Applications 52 (2020): 102500.
  • [6] Yadav, Geeta, and Kolin Paul. “Architecture and security of SCADA systems: A review.” International Journal of Critical Infrastructure Protection 34 (2021): 100433.
  • [7] Lecture: Cyber Security for Smart Grids.
  • [8] T. T. Khoei, G. Aissou, W. C. Hu and N. Kaabouch, “Ensemble Learning Methods for Anomaly Intrusion Detection System in Smart Grid,” 2021 IEEE International Conference on Electro information Technology (EIT), Mt. Pleasant, MI, USA, 2021.
  • [9] A. Aribisala, M. S. Khan and G. Husari, “MACHINE LEARNING ALGORITHMS AND THEIR APPLICATIONS IN CLASSIFYING CYBER-ATTACKS ON A SMART GRID NETWORK,” 2021 IEEE 12th (IEMCON), Vancouver, BC, Canada, 2021.
  • [10] H. N. Monday et al., “The Capability of Wavelet Convolutional Neural Network for Detecting Cyber Attack of Distributed Denial of Service in Smart Grid,” 2021 18th (ICCWAMTIP), Chengdu, China, 2021.
  • [11] AlHaddad, U.; Basuhail, A.; Khemakhem, M.; Eassa, F.E.; Jambi, K. Ensemble Model Based on Hybrid Deep Learning for Intrusion Detection in Smart Grid Networks. Sensors 2023, 23, 7464.
  • [12] Zhai, F.; Yang, T.; Chen, H.; He, B.; Li, S. Intrusion Detection Method Based on CNN–GRU–FL in a Smart Grid Environment. Electronics 2023, 12, 1164.
  • [13] E. Meriaux, D. Koehler, M. Z. Islam, V. Vokkarane and Y. Lin, “Performance Comparison of Machine Learning Methods in DDoS Attack Detection in Smart Grids,” 2022 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, USA, 2022, pp. 1-5, doi: 10.1109/URTC56832.2022.10002244.
  • [14] Y. Guo, C. -W. Ten, S. Hu and W. W. Weaver, “Modeling distributed denial of service attack in advanced metering infrastructure,” 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2015, pp. 1-5, doi: 10.1109/ISGT.2015.7131828.

Please send e-mail to Abdullah Alshra’a with a preference for your talk (if already exists).
Scheduling of talks takes place at the first meeting, Thursday, October 26, 12:15-2:00 p.m., room WHH 04.137-113.