Siber Güvenlik (YL) (Tezli) (İngilizce)
Ders İçerikleri
1. Semester
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| CYS5101 | Cyber Security | 3+0+0 | 9 | Compulsory |
| Basic Concepts in Cyber Security, Basic Security Measures, Cyber Intelligence and Cyber Wars, Cyber Attacks and Defense Methods, Cryptology, Cryptographic Systems, Cryptanalysis, Cyber Security in National Framework, Strategy and Preparation Methods, Risk Analysis, Threat Modeling, Information Security Management Standards | ||||
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| IGE5500 | Scientific Research Methods and Ethics | 3+0+ | 9 | Compulsory |
| 1. Introduction to Research Methods - Overview of scientific research and its importance - Types of research: qualitative, quantitative, and mixed methods 2. Critical Literature Review - Techniques for conducting systematic and critical reviews - Identifying research gaps and formulating research questions 3. Research Philosophy and Paradigms - Understanding positivism, interpretivism, and other paradigms - Aligning research design with philosophical foundations 4. Research Design and Methodology - Developing coherent research frameworks - Selecting appropriate methodologies for research objectives 5. Ethics in Research - Ethical considerations in research design and execution - Gaining access to data and maintaining confidentiality 6. Data Collection Techniques - Sampling strategies: probability and non-probability - Primary data collection: interviews, questionnaires, and diaries - Secondary data evaluation and analysis 7. Data Analysis Methods - Quantitative analysis: statistical tools and techniques - Qualitative analysis: thematic, narrative, and content analysis 8. Writing and Presenting Research - Structuring academic and consultancy reports - Effective presentation techniques for research findings 9. Practical Applications and Case Studies - Hands-on exercises in designing and conducting research - Real-world examples of ethical dilemmas and solutions 10. Final Project - Development of a mini research proposal - Peer presentations and constructive feedback sessions | ||||
2. Semester
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| CYS5102 | Introduction To Cryptography And Security Protocols | 3+0+ | 6 | Compulsory |
| Classical cryptography: some simple cryptosystems, analysis of simple cryptosystems. Shannon theory: probability theory, properties of entropy. Block cipher algorithms: substitution-permutation networks, linear cryptanalysis, differential cryptanalysis, data encryption standard (DES), advanced encryption standard (AES), encryption modes. Cryptographic hash functions: hash functions and data integrity, security of hash functions, message authentication codes. RSA cryptosystem: introduction to public-key cryptosystems, number theory. Public-key cryptosystems based on the discrete logarithm problem: ElGamal cryptosystem, finite fields, elliptic curve cryptosystem. Digital signature: security requirements of digital signature systems, ElGamal digital signature system, DSA, ECDSA. Introduction to post-quantum cryptography. | ||||
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| CYS5104 | Advanced Algorithm Design | 3+0+ | 6 | Compulsory |
| Algorithms and data structures are fundamental to the discipline of computer science. This course introduces students to advanced techniques for designing, analyzing, and evaluating algorithms across a wide range of applications. Topics include algorithm efficiency analysis, brute force and exhaustive search methods, decrease-and-conquer and divide-and-conquer strategies, transform-and-conquer techniques, dynamic programming, greedy algorithms, and iterative improvement methods. Through both theoretical concepts and practical examples, students will develop a deeper understanding of algorithmic problem-solving approaches. | ||||
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| CYS5189 | Seminar | 2+0+ | 12 | Compulsory |
| Problem definition, literature review on the selected topic, article analysis and methodologies, analysis and evaluation of collected data, and reporting. | ||||
3. Semester
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| CYS5291 | Master Thesis 1 | 0+0+ | 30 | Compulsory |
| Literature review and research on the identified topic performed under the supervision of the student’s thesis advisor | ||||
4. Semester
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| CYS5292 | Master Thesis 2 | 0+0+ | 30 | Compulsory |
| Literature review and research on the identified topic performed under the supervision of the student's thesis advisor. | ||||
Department/Programme Elective Courses
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5001 | Artificial Intelligence Principles | 3+0+0 | 9 |
| Akıllı yazılım aracıları ve çok aracılı sistemlerin tasarımı, uygulanması ve seçilmiş uygulamaları. Akıllı davranışın hesaplamalı modelleri, problem çözme, bilgi temsili, akıl yürütme, planlama, karar verme, öğrenme, algılama, eylem, iletişim ve etkileşimi içerir. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5004 | Data Mining | 3+0+0 | 6 |
| This course on data mining covers the fundamental concepts, techniques, and tools used for extracting valuable insights from large datasets through topics such as data preprocessing, exploratory data analysis, predictive modeling, clustering, text mining, and project work. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5006 | Aspects of Deep Learning | 3+0+0 | 6 |
| Students must do projects using Python. Projects will be done on a team basis. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5007 | Data Science | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5009 | Machine Learning | 3+0+0 | 6 |
| This course provides a comprehensive overview of machine learning, covering both supervised and unsupervised learning approaches. It begins with an exploration of supervised learning, addressing regression problems and classification problems, including logistic regression, K-Nearest Neighbor, decision trees, handling imbalanced datasets, random forests, and techniques like cross-validation. The course delves into exploratory data analysis and data pre-processing techniques essential for effective machine learning. It then progresses to advanced topics such as hyperparameter tuning, dimensionality reduction, and unsupervised learning. Ensemble learning methods, particularly boosting techniques, are covered, along with an in-depth study of artificial neural networks, including perceptrons and multi-layer networks. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5010 | Artificial Intelligence of Things | 3+0+0 | 6 |
| The content of the subject includes the following basic topics: Basic Electronics and Hardware Information: Programming Languages: Internet of Things Protocols: Total Data and Processing: Wireless Communication Technologies: Application Development and Platforms: Security and Privacy: Industrial IoT and Applications: | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5011 | Artificial neural networks | 3+0+0 | 6 |
| Artificial Neural Network Architectures Training Algorithms Network Training and Hyperparameter Settings regularization Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN) Deep Learning Applications | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5012 | Human-Computer Interaction | 3+0+0 | 6 |
| Teaching the basic principles of user interfaces. Introduce students to usability models and principles. Get students to carry out user and task analyses. Teach design, prototype development, and evaluation by having students complete term projects. Discuss the effects of interface properties such as color and typography. Teach new user interface techniques. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5013 | Robotic Systems | 3+0+0 | 6 |
| In this course, sub-systems and components of autonomous robots are introduced, motion techniques are taught, applications related to trajectory planning are studied, control strategies for robots are explained, students are informed about new technologies and application areas in robots. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5015 | Optimization Algorithms | 3+0+0 | 6 |
| The content of the course includes the concept of optimization and its uses, the development processes of metaheuristic algorithms, detailed information about the most commonly used algorithms and application examples. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| AO5018 | Machine Learning Operations | 3+0+0 | 6 |
| After completing this course satisfactorily, a student will: 1. Design a well-defined problem formulation for a basic MLOps problem. 2. Solve well-defined problems using MLOps methods and algorithms. 3. Explain basic concepts of MLOps methods. 4. Develop MLOps systems by programming languages. 5. Work as a team in a MLOps project. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| COE5006 | Error-Correcting Codes | 3+0+0 | 6 |
| Linear codes, weights and distances, generator and control matrices, dual codes, Hamming codes, Reed Muller codes, Golay codes, bounds, finite fields, cyclic codes, BCH and Reed Solomon codes, weight distributions. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| COE5010 | Advanced Computer Architecture | 3+0+0 | 6 |
| Basic principles of computer architecture. Design and organization of computer architecture. Running of programs written with high level languages on computer hardware. Using of SPIM simulator. Interrupts, ISA and performance metrics. Single cycle data path, pipeline, pipelined data path and forwarding. Pipeline stallings and Intel Asm. SSE, MMX, caches, virtual memories, parallel programs and OpenMP. I/O, shared memories and instruction level parallelism. Scheduling. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| COE5012 | Parallel Computing | 3+0+0 | 6 |
| Parallel computing methods, algorithms and parallel architectures. Demonstration of parallel programming languages developed for different architectures on sample applications. Performance measurement and analysis of parallel programs. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| COE5015 | Cloud Computing | 3+0+0 | 6 |
| This course will be presented using the “Essentials of CLOUD COMPUTING” book and also several survey papers published by Dr.Mohammad Masdari himself. In addition, this course presents the main challenges in cloud computing and is essential for the students who want to continue their master's degree in this context. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| COE5022 | Internet of Things | 3+0+0 | 6 |
| The course content covers the following basic topics: Basic Electronics and Hardware Information: Programming Languages: Internet of Things Protocols: Data Collection and Processing: Wireless Communication Technologies: Application Development and Platforms: Security and Privacy: Industrial IoT and Applications: | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| COE5025 | Distributed Systems | 3+0+0 | 6 |
| The content provided captures the essence of a course on Distributed Systems. It highlights key aspects such as the distribution of data over a network, the appearance of a single computer to system users, communication through message passing, and various themes including process distribution, data distribution, concurrency, resource sharing, synchronization, and more. It also emphasizes the importance of designing, implementing, and debugging large programming projects as part of the course. Overall, the content provides a good overview of the course's focus and objectives. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| COE5026 | Wireless Adhoc Networks | 3+0+0 | 6 |
| The course Wireless Ad Hoc Networks will set off on an in-depth walk through the realm of wireless communication. The course will begin with the fundamental principles and challenges of ad hoc networks, routing algorithms, transport protocols, wireless internet, and network security. Insights into Quality of Service (QoS) considerations and energy management solutions in ad hoc networks will be offered as the course proceeds. Vehicular ad hoc networks a cutting-edge technology will also be discussed in the course. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| COE5027 | Natural Language Process | 3+0+0 | 6 |
| Regular Expressions, Text Normalization, Edit Distance, N-gram Language Models, Naive Bayes and Sentiment Classification, Vector Semantics and Embeddings, Sequence Labeling for Parts of Speech and Named Entities, Transformers and Pretrained Language Models, Machine Translation, Question Answering and Information Retrieval, Chatbots and Dialogue Systems, Automatic Speech Recognition and Text-to-Speech | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| CYS5002 | Advanced Lınux Kernel Programmıng | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| CYS5004 | Advanced Cryptography | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| CYS5005 | Introductıon To Cryptanalysıs | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| CYS5006 | Blockchaın: Securıty And Applıcatıons | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| CYS5007 | Wıreless Network Securıty | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| CYS5008 | Malware Analysıs | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| CYS5009 | Penetratıon Testıng | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| CYS5010 | Cyber Securıty Law | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| CYS5011 | Cyber Securıty Plannıng And Management | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| CYS5012 | Cyber Securıty | 3+0+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| CYS5013 | Computer Network Securıty | 3+0+0 | 6 |