ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 10 Interactive Classroom: 11 Total: 22
Use languages English
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: Electronics and Computing, External department linked to the degrees
Areas: Computer Science and Artificial Intelligence, Área externa M.U en Intelixencia Artificial
Center Higher Technical Engineering School
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable
· To know different technical and tools to implement solutions based in IA that allow the automated detection of vulnerabilities, attacks, and fraudulent content and applications.
· To know, understand and analyse real application cases of IA techniques in different cybersecurity fields.
· To know techniques that facilitate security by design and that allow a secure systems and networks administration and communications, allow risk management and make possible a fast recovery in front of cybersecurity events.
· To understand the importance of the concept of identity and to know techniques that guarantee the access to the data and their privacy.
Cybersecurity: concepts and introduction.
Models of threat detection and attack prevention.
Detection of fraudulent content and applications.
Data mining in event management systems.
Identity control, biometrics and behaviour patterns.
Anomaly detection and clustering for the detection of communications attacks.
Risk management in IA, critical risks and normality profiles, malicious uses and contingency and recovery plans.
Basic bibliography:
Effective Cybersecurity: A Guide to Using Best Practices and Standards.
William Stallings
Addison-Wesley Professional, 2018
ISBN 978-0134772806
Machine Learning and Security: Protecting Systems with Data and Algorithms, 1ra edición.
Clarence Chio, David Freeman.
O'Reilly, 2018
ISBN 978-1491979907
Mastering Machine Learning for Penetration Testing: Develop an extensive skill set to break self-learning systems using Python, 1ra edición
Chiheb Chebbi,
Packt Publisinh, 2018
ISBN 978-1788997409
Complementary bibliography:
Hands-On Artificial Intelligence for Cybersecurity: Implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies.
Alessandro Parisi
Packt Publishing, 2019
ISBN 978-1789804027
- ENISA. Agencia de la Unión Europea para la Ciberseguridad.
https://www.enisa.europa.eu/
BASIC AND GENERAL
CG1 - Maintain and extend well-founded theoretical approaches to allow the introduction and exploitation of new and advanced technologies in the field of Artificial Intelligence.
CG2 - Successfully address all stages of an Artificial Intelligence project.
CG4 - Elaborate adequately and with a certain originality written compositions or reasoned arguments, draft plans,
work projects, scientific articles and formulating reasonable hypotheses in the field.
CG5 - Work in a team, especially of a multidisciplinary nature, and be skilled in managing time, people and decision-making.
CB6 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of
ideas, often in a research context
CB7 - That the students know how to apply the knowledge acquired and their ability to solve problems in new or little known environments
within broader (or multidisciplinary) contexts related to their area of study.
CB9 - That the students know how to communicate their conclusions and the ultimate knowledge and reasons that support them to specialized and non-specialized audiences in a clear and unambiguous way
CB10 - That the students have the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous.
TRANSVERSAL
CT5 - Understand the importance of entrepreneurial culture and know the means available to entrepreneurs.
CT8 - Assess the importance of research, innovation and technological development in socioeconomic and cultural progress of society.
CT9 - Have the ability to manage time and resources: develop plans, prioritize activities, identify criticism, establish deadlines and meet them.
SPECIFIC
CE4 - Know the fundamentals and basic techniques of artificial intelligence and its practical application.
CE8 - Ability to design and develop secure intelligent systems, in terms of integrity, confidentiality and robustness.
CE19 - Knowledge of different areas of application of AI-based technologies and their ability to offer differentiating added value.
CE20 - Ability to deal with interdisciplinary environments and combine and adapt different techniques, extrapolating knowledge between different fields.
CE21 - Knowledge of the techniques that facilitate the organization and management of AI projects in real environments, the management of resources and task planning in an efficient manner, taking into account concepts of knowledge dissemination and open science.
CE22 - Knowledge of techniques that facilitate the security of data, applications and communications and their implications in different fields of application of AI.
CE30 - Be able to propose, model and solve problems that require the application of methods, techniques and technologies of
artificial intelligence.
Lectures (10 hours):
Oral presentation complemented by the use of audiovisual media and the introduction of some questions addressed to the students, in order to transmit knowledge and facilitate learning. In addition to the oral presentation time by the teacher, this training activity requires the students to dedicate time to prepare and review the class materials on their own.
Interactive classes (11 hours):
Problem-based learning, seminars, case studies and projects: these are sessions whose objective is for students to acquire certain skills based on solving exercises, case studies and carrying out projects that require the application of knowledge and skills developed during the course. These sessions may require students to present orally their solution to the problems raised. The work carried out by the students can be carried out individually or in working groups.
Practical laboratory classes: classes dedicated to students developing practical work that involves addressing the resolution of complex problems, and the analysis and design of solutions that constitute a means of solving them. This activity may require students to present the work done orally. The work carried out by the students can be carried out individually or in working groups.
E1: Final exam 25%
E2: Evaluation of practical work 40%
E3: Evaluation of supervised works 35%
To pass (and release) both E2 and E3, it is necessary to reach 40% of the maximum score provided for these evaluation elements. There is no minimum required for E1.
To pass the subject it is necessary to reach the previous minima (in E2 and E3) and get a minimum of 5 points out of 10 in the final weighted grade.
In the case of not obtaining the minimum required to pass any of the parts (E2 and E3), the student will have a second opportunity in which they will only deliver the elements not passed.
In the case of passing part of the evaluated elements, but not reaching the precise minimum to pass the entire subject, the grade to be included in the official qualification sheet will be calculated as the minimum between the weighted average of the parts passed and 4.9.
The condition of "Presented" will be provided to those who submit all the practices and obligatory works or present themselves to the objective test in the official evaluation period.
The delivery of practices and works must be carried out within the established term, and will follow the specifications indicated in the proposal both for the presentation and for the defense.
In the case of fraudulent completion of exercises or tests, the "Rules for the assessment of academic performance of students and qualification review" of the university will be applied. In application of the corresponding regulations on plagiarism, the total or partial copy of any practice or theory exercise will result in failure in both opportunities of the course, with a grade of 0.0 in both cases.
Academic sessions work time: 21 total hours, divided into 10h (lectures), 11h (Practical sessions).
Individual work time: 54h.
The subject will be taught in English. The theory lectures will be given by UVigo and broadcast to all students. There will be a specific face-to-face interactive teaching group at each university (USC-UDC-UVigo).
Maria Purificacion Cariñena Amigo
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881813563
- puri.carinena [at] usc.es
- Category
- Professor: Temporary PhD professor
| Wednesday | |||
|---|---|---|---|
| 18:30-20:00 | Grupo /CLE_01 | English | IA.12 |
| 01.22.2025 10:30-14:00 | Grupo /CLIL_01 | IA.12 |
| 01.22.2025 10:30-14:00 | Grupo /CLE_01 | IA.12 |
| 07.01.2025 10:30-14:00 | Grupo /CLIL_01 | IA.12 |
| 07.01.2025 10:30-14:00 | Grupo /CLE_01 | IA.12 |