The Internet and Security track revolves around network scenarios where computing applications meet the real world, hyper-connected via the Internet.
The fields investigated in this track include the cyberspace (online social networks, Web applications, big data), cyberservices (the Internet of Things, smart cities, the cloud), and cybersecurity (cryptography, digital forensics).
This track is closely related with the “Multimedia and Digital Arts” one, which you can find here.
A list of the courses available in the “Internet and Security” track for the 2022-2023 academic year is reported below.
There are two mandatory courses: Internet & Multimedia (composed of Internet and Computer vision) and Stochastic Processes. The other courses can be freely chosen while keeping to some basic rules: at least 5 courses among the ICT subjects and 3 courses among the related subjects must be chosen. Furthermore, a soft skill course must be selected, and students must pass the B2 English exam.
It is further advised to choose about 30 ECTS credits per semester to keep your workload balanced.
The Internet & Multimedia course is mandatory.
It is composed of 2 parts: Internet and Computer Vision.
Furthermore, the Stochastic Processes course must also be taken mandatorily.
The subject aims at providing basic knowledge of modern telecommunication architectures, as well as fundamental mathematical tools for the modelling, design and analysis of telecommunications networks and services.
The course will also give you some practical experience with network protocols and devices, thanks to a series of lab experiences that will introduce you to the art of router and socket programming. Ancillary to all this knowledge, the course will help you develop some basic management skills that shall belong to the baggage of each engineer.
Some of the topics that will be considered by the course are data traffic sources, multimedia streams and content, packet switched networks: basics of data networks, ISO/OSI and TCP/IP protocol stacks, congestion control and scheduling algorithms and the application layer.
The computer vision courses presents the principles and techniques for image processing, understanding and analysis.
The course will show how to extract relevant information from visual data that can be used in challenging real world applications like autonomus driving or smart manifacturing.
It presents the mathematical, programming, and technical issues of these tasks and will include a relevant hands-on laboratory part where students will also develop C++ applications based on the OpenCV library.
This is a theoretical course intended to provide knowledge of the main mathematical tools and modeling techniques for the study of telecommunication networks and networking protocols.
The students will get to know the theoretical basics of Markov chains, renewal processes, queueing theory and traffic models. These instruments will be further applied to the analysis of datalink and networking protocols.
Select 5 courses from the following list
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.
For further information, check out the videoclip here
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.
Select 3 courses from the following list
This course reviews the theoretical basis and allows a critical study of the cryptographic protocols used in many applications (authentication, digital commerce).
In the first part, we will give the mathematical basic tools (essentially from elementary and analytic number theory) that are required to understand modern public-key methods. In the second part, we will see how to apply this know-how to study and criticize some protocols currently used.
The course teaches how to design and develop a distributed application for the management of structured data over time.
Programming proficiency (Java or C) is required.
At the end of the course, the student will gain competences concerning databases, their data models and properties, and formal languages for querying a database, and will be able to carry out an actual project for the design and development of a database application using a relational database management system (RDBMS).
Course details will be available soon
This course expands programming skills already acquired by the students to give a special emphasis on scientific programming.
The students will be guided through the object-oriented programming paradigm to design and develop software in the Python language.
It will be given the competence to analyze formal correctness, computability, and complexity of a program, with a clear problem-solving purpose.
A student successfully completing the course should be able to lay down the key aspects differentiating Reinforcement Learning from other machine learning approaches.
Given an application, the student should be able to determine if it can be adequately formulated as a Reinforcement Learning problem, to be able to formalize it as such and to identify a set of techniques best suited to solve the task, together with the software tools to implement the solution.
The objective of the course is to learn methodologies for web design and development, practicing them through the implementation of an actual application. This implies to provide the students with a strong computer science competence on Web engineering, design methodologies and architectural alternatives.
The lab aims to help students improve their oral communication through the study and practice of the elements contributing to successful communication.
The focus is on raising the students' awareness on the importance of verbal and non verbal language in interactions to make communication more effective.
The students will learn the meanings of body language and paralanguage (voice intonation, volume, etc), how they are used in different types of interactions (one-to-one, one-to-many, computer-mediated, etc.), and will have to apply them in a number of assigned tasks.
The lab requires the students' active participation in all class activities, aimed at applying the communication strategies learned.
This course will provide the foundations of the project management.
Traditional (such as the Project Management Institute approach) as well as more advanced techniques - such as the Agile Methodology - will be reviewed. Special focus will be put on the methodologies more suited for the ICT environment.
The learning outcomes are: the understanding of the nexus between media and communication development and democratic principles of access, inclusion, participation, pluralism, and equality; the role and responsibilities of actors involved in the design, development and regulation of media and ICT through a critical reading of the multistakeholder approach; the understanding of trends and prospective visions in the implementation of values and democratic principles in the development of knowledge and communication societies; the acquisition of a gender-aware perspective through which communication processes and practices can be understood.
Select further 18 ECTS credits from courses of this or another curriculum, or any course of the University of Padova coherently with your overall study plan.
You can select exams from the ICT subjects, the related ones or any other course from the University of Padova coherent with the study plan.
Students are asked to carry out a substantial individual project in their final year. The project can be carried out either at the University of Padova (30 ECTS combining a 21 ECTS Final Project and a 9 ECTS Report), or in an external institution, such as an Industry or a Research Center, either national or international (30 ECTS combining a 21 ECTS Final Project and a 9 ECTS Internship).
It is also possible to do the internship in an external institution, and the final project at the University, though we suggest to carry out the whole work in a single place.