Courses and Descriptions


First Semester

Instructor: Professor Christos Panayiotou

The purpose of this course is to familiarize the students with some of the main techniques for estimating the state of a dynamical system and use the state of estimation to detect faults in some of the system’s components such as sensor faults and water leaks. Examples will be derived from critical infrastructure systems: power and energy systems, transportation systems and water networks. The students will learn to design and implement (in MATLAB) state estimators and fault detection algorithms for various systems, as well as to model faulty components. 

Instructor: Professor Alessandro Astolfi

This course introduces finite-dimensional optimization and decision theory and basic optimization algorithms. The formulation of optimization problems arising in CISs is also presented together with worked out examples. 

After the course the students will be able to formulate optimization problems, design computer algorithms for finding minima and maxima in a wide range of optimization problems involving smooth criteria and, just as importantly, to interpret, and if necessary modify, the algorithms found in standard computer packages. The students will also be able to formulate and solve decision making problems and problems involving graphs. Finally, the students will be capable of formulating optimization problems arising in CISs and to compute their solutions.

Instructor: Professor Chris Hankin

The aim of the course is to cover the underlying principles and techniques used in securing CIS and to give examples of how they are applied in practice.
At the end of the course, the students will have an understanding of the themes and challenges of CIS security and the current state of the art, they will have developed a critical approach to the analysis of CIS security and will be able to bring this approach to bear on future decisions regarding security. Finally students will be able to appreciate the main threats, attack techniques and defenses relevant to the security of CIS, to identify potential vulnerabilities and propose countermeasures and to design secure CIS.

Instructor: Professor Georgios Ellinas

This course provides a solid understanding on the fundamentals of the following critical infrastructure systems: electric power systems, telecommunication networks, water distribution networks, and transportation networks. To understand how to model and simulate simple instances of these networks. It introduces general tools for modeling such systems (automata, Petri-nets, graph theory, conservation laws, differential and algebraic equations, partial differential equations) and general tools for simulating and analyzing such systems (discrete event simulation, steady-state methods, state-space, design of algorithms).
By the end of the course students will obtain the fundamentals skills required to model the most important critical infrastructure system components and the systems as a whole for the following infrastructures: electric power systems, telecommunication networks, water distribution networks, and transportation networks. They will also be able to simulate simple cases for these systems under steady state and faulty conditions.

Second Semester

Instructor: Professor Thomas Parisini

The aim of the course is to provide advanced elements of industrial control systems with emphasis on controlling generic large-scale systems related to critical infrastructures. Theory of multi-variable control is given with emphasis on optimal and model-predictive control approaches, as well as insight on the basic architectures of modern multi-level software automation architectures. The automation SW architectures and technologies are put in the context of CIS use cases where appropriate.
The students, at the end of the course, should know the basic principles governing the analysis and design of multivariable control systems in the context of large-scale systems. 
They should be able to carry out the static and dynamic analysis characterization of models to be used in the design of multi-variable control systems. Moreover, they should be able to evaluate, among several options, how to configure and design the architecture and the controller of a multi-variable automatic control system starting from requirements and considering technological constraints.

Instructor: Professor Marios Polycarpou

This course aims to introduce the theory, methods and applications of the field of Machine Learning. The objectives of the course are the presentation of the core principles and algorithms of supervised, unsupervised and reinforcement learning, the explanation of the application of these algorithms for the solution of regression, classification, clustering and decision-making problems and the demonstration of practical machine learning tools suitable for the analysis of data sets and the solution of machine learning problems. Special emphasis will be placed on real-world critical infrastructure systems applications.
By the end of the course, students should be able to understand the principles of supervised, unsupervised and reinforcement learning, to design and implement a wide variety of machine learning algorithms, to analyze raw data to create representations that are more suitable for machine learning algorithms and to solve and evaluate the performance of classification, regression, dimensionality reduction and clustering problems that arise in critical infrastructure systems using state-of-the-art machine learning tools.

Instructor: Dr. Daina Nikolaou

The purpose of this course is to explore the many dimensions of new venture creation and growth. While most examples will be drawn from new venture formation, the course examines cases in ICT-related entrepreneurship, as well as social and non-profit entrepreneurship.

The course also focuses on the challenges involved in attempting to profit from both incremental routine information and more radical revolutionary changes in products and processes. It highlights the importance of innovation to new ventures as well as established firms, and explores the organizational, economic and strategic problems that need to be tackled in order to ensure innovation as a long-term source of competitive advantage.
This course will give students a thorough knowledge of where innovation can be found within the organization, how to recognize it, and how it can be used for competitive advantage. Moreover, will provide students an understanding of how they, as future leaders of innovative organizations, can recognize and harness creativity. 

Instructor:  Professor Georgios Ellinas

This purpose of this course is to provide a solid understanding of the following critical infrastructure systems: electric power systems, telecommunication networks, water distribution networks, and transportation networks. To model and analyze these systems using advanced network simulators. To understand the practical problems in their control, and management, and to obtain practical skills related to the design and operation of these systems under normal and faulty conditions.

The students are expected to be able to model the most important critical infrastructure system components and be able to analyze them under steady state conditions. Moreover, they should be able to design and simulate these systems according to given operational criteria and constraints. Finally students should understand the technical, economic, and environmental implications of the design and operation of critical infrastructure systems. 

Third Semester

Coordinator: Associate Professor Maria K. Michael

The MSc thesis is a final-year project which enables students to carry out research in order to deepen their scientific and applied knowledge and skills in a specific topic in the area of Intelligent Critical Infrastructure Systems (CIS).

Through their research students will understand technical and management features in Intelligent CIS, learn to deal with particular challenges in Intelligent CIS and obtain experience in research methods, including technical writing and communication skills, as well as project management.

Entire Duration of Programme

Coordinator: Associate Professor Maria K. Michael

Seminars exploring current research and topical issues in the areas of monitoring, control, management, and security of CIS, as well as other related electrical and computer engineering disciplines, focused on the general theme of innovation. Seminars are organized in blocks with related content, and are presented by prominent outside speakers as well as by faculty members and graduate students. 

The course requires participation in at least 15 seminar presentations over the course of the MSc program. Students must attend at least 5 non-technical seminar presentations. Students are also expected to participate in a dedicated workshop, organized at the University of Cyprus, which will be exploring specific research and innovation topics related to their MSc program. The workshop will include prominent speakers from the academia and industry. During the workshop, students will also be required to showcase their work for their MSc thesis, attend the presentations by other fellow MSc students, and discuss their research work and exchange ideas with other students and faculty.