CYBER SECURITY (ENGLISH, THESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
CYS5172 Advanced Computer Forensics Fall 3 0 3 7
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi AHMET NACİ ÜNAL
Course Objectives: To teach advanced level computer forensic techniques.

Learning Outputs

The students who have succeeded in this course;
Effective use of artificial intelligence in the areas of forensics, in this context, ensure research in the field of artificial intelligence.

Course Content

Natural and Artificial Intelligence. Intuitive Problem Resolution. Information Modeling and Predicate Logic. Logic Programming. Applicability of Expert Systems and Forensic Computing. Applicability of Artificial Neural Networks and Forensic Computing. Applicability of Genetic Algorithms and Forensic Computing. Applicability of Fuzzy Logic and Forensic Computing. Applicability of Natural Language Processing and Forensic Computing. Applicability of Audio Processing and Forensic Computing. Applicability of Image Processing and Forensic Computing. Biometry Artificial Intelligence Applications I (Face Detection). Biometry Artificial Intelligence Applications II Fingerprint). Biometry Artificial Intelligence Applications III Character Recognition, Handwriting).

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Natural and Artificial Intelligence Lecturer notes
2) Intuitive Problem Resolution Lecturer notes
3) Information Modeling and Predicate Logic Lecturer notes
4) Logic Programming Lecturer notes
5) Applicability of Expert Systems and Forensic Computing Lecturer notes
6) Applicability of Artificial Neural Networks and Forensic Computing Lecturer notes
7) Applicability of Genetic Algorithms and Forensic Computing Lecturer notes
8) Applicability of Fuzzy Logic and Forensic Computing Lecturer notes
9) Applicability of Natural Language Processing and Forensic Computing Lecturer notes
10) Applicability of Audio Processing and Forensic Computing Lecturer notes
11) Applicability of Image Processing and Forensic Computing Lecturer notes
12) Biometry Artificial Intelligence Applications I (Face Detection) Lecturer notes
13) Biometry Artificial Intelligence Applications II Fingerprint) Lecturer notes
14) Biometry Artificial Intelligence Applications III Character Recognition, Handwriting) Lecturer notes

Sources

Course Notes: Network Forensics, Ric Messier. 2017. Mobile Forensic Investigations: A Guide to Evidence Collection, Analysis, and Presentation. Lee Reiber, 2016.
References: Ders notları

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance 10 % 0
Laboratory 0 % 0
Application 0 % 0
Field Work 0 % 0
Special Course Internship (Work Placement) 0 % 0
Quizzes 0 % 0
Homework Assignments 4 % 10
Presentation 1 % 10
Project 0 % 0
Seminar 0 % 0
Midterms 1 % 20
Preliminary Jury 0 % 0
Final 1 % 60
Paper Submission 0 % 0
Jury 0 % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Laboratory 0 0 0
Application 0 0 0
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 14 12 168
Presentations / Seminar 2 3 6
Project 0 0 0
Homework Assignments 4 8 32
Quizzes 0 0 0
Preliminary Jury 0 0 0
Midterms 1 20 20
Paper Submission 0 0 0
Jury 0 0 0
Final 1 20 20
Total Workload 288

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications.
1) To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field.
1) Being able to independently carry out a work that requires expertise in the field.
1) To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning.
1) To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data.
2) To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines,
2) To be able to develop strategy, policy and implementation plans in the fields related to the field and to evaluate the obtained results within the framework of quality processes.
2) To be able to critically examine social relations and the norms that guide these relations, to develop them and take action to change them when necessary.
2) To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.
2) To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility.
2) To be able to comprehend the interdisciplinary interaction with which the field is related.
3) To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies.
3) Being able to lead in environments that require the resolution of problems related to the field.
3) To be able to solve the problems encountered in the field by using research methods.