Application of Machine Learning in Information Security

1.

Subject title

Application of Machine Learning in Information Security

Примена на машинско учење во информациска безбедност

2.

Code

m23_w_052

3.

Study program

Cloud Computing, IT management, Bioinformatics, Еducation with ICT, Eco-informatics, Inteligent Systems, Internet Technologies and cyber security, Computer Science, Statistics and Data Analytics, Software for embedded systems, Software Engineering, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Software Engineering, Data science in computer science and engineering, Security, Cryptography and Coding,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Александра Поповска Митровиќ ворн. проф. д-р Христина Михајлоска проф. д-р Весна Димитрова

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

By completing the content of the subject the student will acquire knowledge of the basic principles and standard safety models of proven security. Will also study the basic Reduction techniques in proving as well as sufficient and necessary assumptions for evidence. At the end of the course the student will be able to design a security strategy from the beginning. To write a complete basic security evidence. Will certainly be able to draw a conclusion from the influence of evidence on the safety of primitive.

11.

Subject content:

1. Introduction to proven security 2. Method of proving security 3. Hostile models 4. Safety assumptions and reductions 5. Game-Based reliability 6. Pseudo/random functions and permutations. One -way functions 7. Proving techniques in symmetric cryptography 8. Proving techniques in cryptography with public key

12.

Learning methods:

Предавања, проекти, дискусии, работилници

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

60 + 0 + 45 + 45 + 30 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

30 hours

17.

Grading method

17.1.

Tests

0 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

20 points

17.4.

Final exam

50 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

Домашна работа

20.

Language of instruction

Македонски и англиски

21.

Quality assurance method

Механизам за интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature