Cybersystems – Internet and Security

The course will explore both technological and methodological aspects related to the design/dimensioning of modern and upcoming communication networks and protocols, touching upon cutting-edge topics such as network funtion virtualization, Quality-of-Experience maximization, cross-layer and cognitive networking, all of this framed by a solid mathematical framework whose application potential goes well beyond the horizon of network design.

This course presents machine learning and signal processing technologies that can be used handling digital evidences in investigations.

The aim is to provide students with a set of analysis strategies for hard disk, network streams, image and video data (including authentication and fake detection), data from social networks.

These techniques are reviewed and discussed both theoretically and via some case studies.

The course entails the intervention of legal specialists that describe the procedural implications of the technical analysis and the juridical consequences on expert decisions.

The course exploits basic signal analysis knowledge that the student is assumed to have acquired from previous studies to explore advanced concepts in the field of digital signal processing.

The course will review Z-transform, linear time-invariant systems, FIR/IIR filters, to investigate the design and usage of digital filters, interpolation/decimation of digital signals, frequency analysis of digital signals.

Practical application examples, useful in many areas of information engineering, will be provided.

Game theory is the science of analyzing multi-objective multi-agent problems (i.e., "games").

This involves the games we usually play for fun in our everyday life, but in a more serious context is applied to resource competition, distributed management, efficient allocation over multi-user systems and/or communication networks.

This course teaches all the basic concepts, as well as some advanced ones, of game theory. Also, it applies them to scenarios of interest for ICT.

The objective of the course is to offer a broad view of the potential of ICT in industry and business domains, through a well-structured sequence of frontal academic lectures, seminars offered by professionals working in top-tier industrial sectors, and lab experience with industrial development kits and cutting-edge networking devices.

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In modern communication systems, securing against malicious behavior is a primary issue, and must be part of the design since the beginning, rather than a patch added as a belated measure.

The class introduces fundamental notions and tools in information security, with a focus on the solutions, attacks, and countermeasures that can be deployed at the different layers in modern communication networks.

The students will be asked to apply their acquired knowledge to practical use cases, industrial standards, and experimental scenarios.

This course provides knowledge of the concepts of the "IoT" and "Smart cities," describing their scientific and market trends, as well as the application of these paradigms in practical ICT context. The students will learn about some key platforms and standards (ZigBee, 6LoWPAN, WiFi, Bluetooth Low Energy, SigFox, Lo-Ra), and will review their applications for home automation, industrial applications, autonomous driving, urban monitoring, privacy and security.

Intelligent systems capable of automated reasoning are emerging as the most promising application of ICT. The aim of this course is to provide fundamentals and basic principles of the machine learning problem as well as to introduce the most common techniques for regression and classification.

Both supervised and unsupervised learning will be covered, with a brief outlook into more advanced topics such as Support Vector Machines, neural networks and deep learning. The course will be complemented by hands-on experience with Python programming.

The course gives instruments for theoretical analysis and computer simulation of distributed systems.

During the classes, simulation software and mathematical tools will be reviewed, and applied to scientific examples taken from the literature.

The expected outcome for the student is to master different characterizations of network systems and their performance metrics, result interpretation, and design of optimization criteria.

The course deals with model coding theory, presenting code on sparse graphs such as Low Density Parity Check Codes (LDPC) and the related decoding algorithms, as well as Fountain Codes, a class of linear random codes that operate on data packet objects.

Further, advanced topics such as linear random coding on networks will be treated, as a means to store or distribute data packets in a network, in a way that is robust to link errors and node failures.

The course describes networking phenomena over many scenarios.

Although communication networks and the Internet are primary references, similar representations can be used for social networks, ecological systems, and epidemic diseases.

The course describes network generation models, and then community structures are reviewed, also outlining the applications to online social communities, brain networks, and biological systems.

The course covers the theory and practice of modern artificial neural networks, highlighting their relevance both for machine learning applications and for modeling human cognition and brain function.

Topics include single-neuron modeling and principles of neural encoding; supervised, unsupervised and reinforcement learning; feed-forward and recurrent networks; energy-based models; large-scale brain organization.

Theoretical discussion of various types of network architectures and learning algorithms is complemented by hands-on practices in the computer lab (PyTorch framework).

The course aims at providing some advanced knowledge on network protocols for wireless communications, including the analysis of distributed wireless networks, state of the art wireless technologies and current trends.

The topics that will be covered range from link layer technology to routing over ad hoc wireless networks and application layer.