| Teaching Staff
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| Language of Instruction |
Türkçe (Turkish) |
| Type Of Course |
Compulsory |
| Prerequisites |
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| Recommended Optional Programme Component |
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| Course Objectives |
With this course, the student will learn social engineering techniques, foresee cyber threats and attacks that may come from social engineering world, develop countermeasures, learn social network analysis techniques and methods, gain theoretical and practical knowledge in order to determine vulnerability of the network and to take security-enhancing measures. |
| Course Content |
With this course, the student; recognize the world and methods of social engineering, which is a discipline based on weaknesses; will have knowledge on developing measures against social engineering; will be able to handle the network structure from the perspective of social networks; will be able to calculate analysis metrics on the network and visualize and interpret the analysis results using Gephi software. |
| Learning Outcomes (LO) |
• To be able to prevent social engineering initiatives by determining the areas of weakness.
• To be able to increase the awareness of the personnel in the institution where they will work in business life.
• Modeling the data of a social network with the Graph data structure.
• To be able to calculate the centrality metrics of the network and interpret the results.
• To be able to visualize and analyze networks using Gephi and NodeXL software and interpret their results.
• To be able to identify communities in the network.
|
| Mode of Delivery |
Distance Learning |
| Course Outline |
| Week |
Topics |
| 1. Week |
Introduction to Social Engineering and Social Network Analysis |
| 2. Week |
Social Engineering Basics |
| 3. Week |
Social Engineering Techniques and Methods |
| 4. Week |
Information Gathering Techniques |
| 5. Week |
Social Networks Basics |
| 6. Week |
Network Structures Fundamentals |
| 7. Week |
Network Types |
| 8. Week |
Mid-Term Exam |
| 9. Week |
Network Analysis – Whole Network Metrics |
| 10. Week |
Network Analysis – Node Metrics |
| 11. Week |
Community Detection in Social Networks |
| 12. Week |
Gephi Tool (Constructing, Visualizing and Analyzing Social Networks) |
| 13. Week |
Gephi Tool (Constructing, Visualizing and Analyzing Social Networks) |
| 14. Week |
NodeXL Tool (Constructing, Visualizing and Analyzing Social Networks) |
|
| Assessment |
| |
Percentage(%) |
| Mid-term (%) |
40 |
| Quizes (%) |
|
| Homeworks/Term papers (%) |
20 (Final'e İlave) |
| Practice (%) |
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| Labs (%) |
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| Projects/Field Work (%) |
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| Seminars/Workshops (%) |
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| Final (%) |
60 |
| Other (%) |
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| Total(%) |
100 |
|
| Course Book (s) and/or References |
1. “Kullanıcı Güvenliği Eğitimi”, TÜBİTAK BİLGEM Siber Güvenlik Enstitüsü
2. “Sosyal Mühendisin Maskesini Düşürmek”, Christopher Hadnagy.
3. “Sosyal Mühendislik Saldırıları”, Okan Yıldız, Alper Başaran.
4. “Sosyal Ağ Analizi”, Prof.Dr. Necmi GÜRSAKAL.
5. Ders sunuları ve notları |
| Work Placement(s) |
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| The Relationship between Program Qualifications (PQ) and Course Learning Outcomes (LO) |
| | PÇ1 | PÇ2 | PÇ3 | PÇ4 | PÇ5 | PÇ6 | PÇ7 | PÇ8 | PÇ9 | PÇ10 | | ÖÇ1 | | 2 | 5 | | 3 | | 5 | 1 | 5 | 5 | | ÖÇ2 | | 5 | 5 | | 3 | | 3 | 3 | 5 | 5 | | ÖÇ3 | 5 | | 5 | 4 | 2 | | 5 | | 2 | | | ÖÇ4 | | 5 | 5 | 5 | 5 | 4 | | | 1 | | | ÖÇ5 | 4 | | 4 | 3 | 5 | | 4 | 2 | | | | ÖÇ6 | 5 | | 5 | 5 | 5 | 4 | 5 | 5 | | 1 |
|