Educational guide
IDENTIFYING DATA 2019_20
Subject CURRENT TRENDS AND EMERGING THREATS IN CYBERSECURITY Code 01733112
Study programme
1732 - MASTER UNIVERSITARIO EN INVESTIGACION EN CIBERSEGURIDAD (OL)
Descriptors Credit. Type Year Period
6 Optional Second First
Language
Castellano
Prerequisites
Department MATEMATICAS
Coordinador
MUNOZ CASTANEDA , ANGEL LUIS
E-mail amunc@unileon.es
ncasg@unileon.es
asuac@unileon.es
Lecturers
CASTRO GARCIA, NOEMI DE
MUNOZ CASTANEDA , ANGEL LUIS
SUAREZ CORONA , ADRIANA
Web http://
General description
Tribunales de RevisiĆ³n
Tribunal titular
Cargo Departamento Profesor
Presidente MATEMATICAS GOMEZ PEREZ , JAVIER
Secretario MATEMATICAS SAEZ SCHWEDT , ANDRES
Vocal MATEMATICAS TROBAJO DE LAS MATAS , MARIA TERESA
Tribunal suplente
Cargo Departamento Profesor
Presidente MATEMATICAS SUSPERREGUI LESACA , JULIAN JOSE
Secretario MATEMATICAS LOPEZ CABECEIRA , MONTSERRAT
Vocal MATEMATICAS GARCIA FERNANDEZ , ROSA MARTA

Competencies
Type A Code Competences Specific
  A17093
Type B Code Competences Transversal
  B5220
  B5221
  B5222
  B5223
  B5224
  B5225
Type C Code Competences Nuclear
  C1
  C2
  C3
  C4
  C5

Learning aims
Competences
To Know about the current trends in Cybersecurity. A17093
B5224
B5225
C1
C2
C4
C5
To be able to communicate their conclusions. B5220
B5221
B5222
B5223
C3
To know and develop Python algorithms A17093
B5223
B5224
B5225
C1
C2
C4
C5
To identify when a problem can be solve using Machine Learning techniques A17093
B5223
B5224
B5225
C1
C2
C4
C5
To know the basic models of Machine learning and their mathematical foundations A17093
B5224
B5225
C1
C2
C4
C5
To implement the basic models in Python and to be able to use the corresponding libraries A17093
B5223
B5224
B5225
C1
C2
C4
C5
To know and know how to use the main metrics for the model selection A17093
B5223
B5224
B5225
C1
C2
C4
C5
To identify settings where Big Data can be applied. A17093
B5223
B5224
B5225
C1
C2
C4
C5
To know the basics about Blockchain technology and its main applications A17093
B5224
B5225
C1
C2
C4
C5
Know the Scientific method. Ability to find information and relevant references and to write scientific papers. Organize and prepare scientific conferences
Know the legislation regarding intellectual property, research ethics, patents and utility models. Transfer of research results to companies, innovation

Contents
Topic Sub-topic
1. Introduction to Machine Learning I: Introduction to Python
II: Introduction to Machine Learning applied to Cybersecurity. Supervised learning, error and noise, Overfitting, Regularization, Validation, Introduction to unsupervised learning.
2. Introduction to Big Data III: Introduction to Big Data: introduction and technologies. Case studies.
3. Introduction to Blockchain IV: Introduction to Blockhain technology and its applications.

Planning
Methodologies  ::  Tests
  Class hours Hours outside the classroom Total hours
Portfolios/Learning folder 2 21 23
 
Laboratory practicals 19 41 60
Presentations / expositions 5 20 25
Assignments 2 20 22
 
Lecture 20 0 20
 
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies   ::  
  Description
Portfolios/Learning folder Technique for learning how to compile and organize evidence that encourages the student to reflect on his or her progress and demonstrates that he or she has achieved the professional competences which enable him or her to work professionally.
Laboratory practicals Computer practices based in the topics covered in the lectures, assisted by the professor.
Presentations / expositions Project presentations
Assignments Written assignments related to the topics covered in lectures.
Lecture Description of the contents of the subject.

Personalized attention
 
Lecture
Description
Via email or using moodle

Assessment
  Description Qualification
Assignments Written assignments related to the topics covered in lectures. 45%
Portfolios/Learning folder Technique for learning how to compile and organize evidence that encourages the student to reflect on his or her progress and demonstrates that he or she has achieved the professional competences which enable him or her to work professionally. 20%
Presentations / expositions Project presentations 20%
Laboratory practicals Practical application of the theory of a knowledge area in a particular context. Practical exercises using ICTs. 15%
Others Peer-review activities. Continuous assessment. 20%
 
Other comments and second call
<p>The grade will be computed attending to two types of assessment: summative and continuous. In order to pass the course, it is necessary to obtain a grade of at least 50%. The final grade will be computed only if the student has a grade of at least 40% in every assignment and test.  </p><p>For the continuous assessment, the student will need to create a portfolio, consisting on several assignments to be done along the semester, which will count for 20% of the grade. </p><p>The sumative part will consist on a written final project and two assignments, accounting for 45% of the total grade and their oral presentations, will account for 20% of the grade. </p><p>The grade of the second call will be the graded in the same way.  </p><p>At any given time, an explanation / clarification of the documents submitted by the student may be required. Students are supposed to know and accept the Regulations on Plagiarism of the University.</p><p>The use of any electronic device (cell phones, tablets, etc) allowing the student to have communication with other people will be forbidden while doing the tests, as well as any material not explicitly allowed by the professor. </p><p> If a student breaks this rule, he will fail the exam and the Academic Authority of the Center will be informed so that they can follow the procedure approved by the Governing Council of the University on January 29th, 2015.</p><div><br /></div>

Sources of information
Access to Recommended Bibliography in the Catalog ULE

Basic
Research papers on the most relevant topics published in the last years.
Complementary


Recommendations