In order to pass (and release) both laboratory practices and supervised projects, it is necessary to reach 40% of the maximum score foreseen for these evaluation items. There is no minimum required for the objective test.

In order to pass the subject it is necessary to reach the previous minima (laboratory practices and supervised project) and to get a minimum of 5 points out of 10 in the final weighted grade.

In case of not reaching the minimum required to pass any of the parts (laboratory practices and/or supervised project), the students will have a second opportunity in which they will only deliver the items that were not passed.

In case of passing part of the evaluated items, but not reaching the minimum required to pass the whole subjet, 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 students who submit all the compulsory practicals and assignments or takes the objective test during the official evaluation period.

The delivery of practices and assignments 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.

The professors will facilitate, to the best possible option and within the schedules established for the subject, attendance to the theory and practice groups that best fit the needs of the students who are enrolled part-time, for whom the form of evaluation established here also applies. Students with academic waiver of attendance exemption must attend all the assessment tests.

In case of fraudulent performance of exercises or tests, once it is demonstrated, will imply a failing grade (numerical grade 0) in the call in which it is committed, whether the commission of the fault occurs in the first opportunity or in the second one.

The subject will be taught in English. The theory lectures will be given by UVigo and broadcasted to all students. There will be a specific face-to-face interactive teaching group at each university (USC-UDC-UVigo).

Learning outcomes	Study programme competences
Know different techniques and tools to implement AI-based solutions for automated detection of vulnerabilities, attacks and fraudulent content and applications	AC4 AC8 AC19 AC20 AC22 AC30	BC1 BC6 BC7 BC10	CC5 CC8
Know, understand and analyze real application cases of AI techniques in different cybersecurity fields	AC4 AC8 AC19 AC20 AC21 AC22 AC30	BC2 BC4 BC5 BC6 BC7 BC9 BC10	CC5 CC8 CC9
Know techniques that facilitate security by design and that allow secure system and network communications administration, risk management and a fast recovery upon cybersecurity events	AC8 AC20 AC21 AC22 AC30	BC1 BC2 BC4 BC5 BC7 BC9	CC8 CC9
Understand the importance of identity as a concept and know techniques that guarantee access to the data and their privacy	AC4 AC8 AC21 AC22 AC30	BC1 BC2 BC6 BC7 BC9 BC10	CC8

Topic	Sub-topic
Theory	- Cybersecurity: concepts and introduction - Threat detection and attack prevention models - Detection of fraudulent content and applications - Data mining in event management systems - Identity control, biometrics and behavioral patterns - Anomaly detection and clustering for attack detection in communications - Risk management in AI, critical risks and regular profiles, malicious uses, and contingency and recovery plans
Practice	- Use of specific tools related to cybersecurity environments - Application of AI techniques to solve cybersecurity problems

Methodologies / tests	Competencies	Ordinary class hours	Student’s personal work hours	Total hours
Guest lecture / keynote speech	A5 A9 A20 A21 A22 A23 B1 B2 B6 B10 C5 C8	10	10	20
Laboratory practice	A5 A9 A20 A21 A22 A23 A31 B1 B2 B4 B5 B6 B7 B9 C9	5.25	15.75	21
Problem solving	A5 A9 A20 A21 A22 A23 A31 B1 B2 B4 B5 B6 B7 B9 C9	5.25	15.75	21
Objective test	A5 A9 A20 A21 A22 A23 A31 B1 B2 B4 B6 B7 B9 B10	2	10	12

Personalized attention		1	0	1

(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies	Description
Guest lecture / keynote speech	Oral presentation complemented by the use of audiovisual media and bringing up some questions addressed to the students, in order to transmit knowledge and facilitate learning. In addition to the oral presentation carried out by the professor, this activity requires the students to dedicate time to prepare and review the class materials on their own.
Laboratory practice	Classes dedicated to the development of practical work involving the resolution of complex problems, as well as the analysis and design of solutions that constitute a means for their resolution. This activity may require students to make an oral presentation of the work done. These works can be carried out, depending on the case, either individually or in working groups.
Problem solving	These are sessions in which the objective is that students acquire certain competencies based on the resolution of exercises, case studies and projects that require the application of the knowledge and competencies developed during the course. This activity may require students to make an oral presentation of the work done. This work may be done, depending on the case, either individually or in work groups.
Objective test	Examination in which both theoretical and practical aspects seen during the course can be evaluated.

Methodologies	Competencies	Description	Qualification
Laboratory practice	A5 A9 A20 A21 A22 A23 A31 B1 B2 B4 B5 B6 B7 B9 C9	Assessment of practical assignments (E2)	40
Objective test	A5 A9 A20 A21 A22 A23 A31 B1 B2 B4 B6 B7 B9 B10	Final examination (E1)	20
Problem solving	A5 A9 A20 A21 A22 A23 A31 B1 B2 B4 B5 B6 B7 B9 C9	Assessment of supervised projects (E3)	40

Assessment comments
In order to pass (and release) both laboratory practices and supervised projects, it is necessary to reach 40% of the maximum score foreseen for these evaluation items. There is no minimum required for the objective test. In order to pass the subject it is necessary to reach the previous minima (laboratory practices and supervised project) and to get a minimum of 5 points out of 10 in the final weighted grade. In case of not reaching the minimum required to pass any of the parts (laboratory practices and/or supervised project), the students will have a second opportunity in which they will only deliver the items that were not passed. In case of passing part of the evaluated items, but not reaching the minimum required to pass the whole subjet, 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 students who submit all the compulsory practicals and assignments or takes the objective test during the official evaluation period. The delivery of practices and assignments 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. The professors will facilitate, to the best possible option and within the schedules established for the subject, attendance to the theory and practice groups that best fit the needs of the students who are enrolled part-time, for whom the form of evaluation established here also applies. Students with academic waiver of attendance exemption must attend all the assessment tests. In case of fraudulent performance of exercises or tests, once it is demonstrated, will imply a failing grade (numerical grade 0) in the call in which it is committed, whether the commission of the fault occurs in the first opportunity or in the second one. The subject will be taught in English. The theory lectures will be given by UVigo and broadcasted to all students. There will be a specific face-to-face interactive teaching group at each university (USC-UDC-UVigo).

Basic	William Stallings (2018). Effective Cybersecurity: A Guide to Using Best Practices and Standards. Addison-Wesley Professional

Complementary	Alessandro Parisi (2019). Hands-On Artificial Intelligence for Cybersecurity: Implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies. Packt Publishing