| Course Objectives: |
The aim of this course is to provide students with theoretical knowledge in artificial intelligence and cybersecurity, while also equipping them with the competence to develop practical solutions for real-world problems. Students will learn to use cybersecurity tools and simultaneously develop security bots, web applications, and analysis tools using Python programming, thereby acquiring technical skills highly sought after in the industry. By the end of the course, students will have the capacity to work independently, identify security vulnerabilities, and produce automated security solutions. |
| Course Content: |
This course begins with fundamental concepts of artificial intelligence and cybersecurity, followed by instruction in the Kali Linux operating system and basic terminal commands. Google Dorking techniques for passive information gathering, active information gathering and network scanning methods using Nmap are covered. Phishing attacks and social engineering techniques using GoPhish, followed by SQL injection vulnerabilities and web application security testing with Burp Suite are examined. After the midterm exam, students complete five different projects using Python programming: Telegram security bot (regex-based controls), Discord log analysis bot, Flask-based URL security checker web application, terminal-based CLI security scanning tool, and email phishing analysis system. All projects are completed in each student's independent working environment using completely free tools and libraries. |
Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:
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| Knowledge
(Described as Theoritical and/or Factual Knowledge.)
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1) Define and explain fundamental concepts, attack types, and security vulnerabilities in artificial intelligence and cybersecurity.
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| Skills
(Describe as Cognitive and/or Practical Skills.)
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1) Perform security tests using cybersecurity tools such as Kali Linux, Nmap, and Burp Suite, and apply passive/active information gathering techniques.
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2) Develop security bots, web applications, and automated analysis tools using Python programming language.
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| Competences
(Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)
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1) Plan, develop, and complete cybersecurity projects independently; take responsibility in identifying security vulnerabilities.
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2) Continuously develop oneself by following new technologies and current threats in cybersecurity, and learn solutions to encountered problems.
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3) Test phishing, SQL injection, and other attack techniques in compliance with ethical guidelines, and produce automated security solutions.
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4) Report security analyses and findings in an understandable manner for different audiences, and communicate effectively in team work.
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| Week |
Subject |
Materials Sharing * |
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Related Preparation |
Further Study |
| 1) |
Introduction to AI and Cybersecurity: CIA Triad, Threat Modeling, and AI Applications |
Online Beykoz
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| 2) |
Kali Linux and Basic Terminal Commands: File Management and Package Installation |
Online Beykoz
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| 3) |
Google Dorking and Passive Reconnaissance: OSINT Tools and Social Media Intelligence |
Online Beykoz
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| 4) |
Active Reconnaissance and Network Scanning with Nmap: Port Scanning, Service Detection, and NSE Usage |
Online Beykoz
|
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| 5) |
Phishing and Social Engineering: Designing and Analyzing Campaigns with GoPhish |
Online Beykoz
|
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| 6) |
SQL Injection and Burp Suite Basics: Web Security and Proxy Configuration |
Online Beykoz
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| 7) |
Advanced Burp Suite Usage: Security Testing with Repeater, Intruder, and Scanner |
Online Beykoz
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| 8) |
Midterm Exam |
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| 9) |
Telegram Bot with Python: AI-Based Security Awareness and Attack Scenario Chatbot (Rule-Based) |
Online Beykoz
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| 10) |
Student Industry Meeting -
Log Analysis with Python: Parsing, Feature Extraction, and AI-Assisted Suspicious Behavior Classification (Basic Model) |
Online Beykoz
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| 11) |
Flask Web Application: URL Phishing Detection and AI-Assisted Risk Scoring System |
Online Beykoz
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| 12) |
CLI Security Tool Development: Kaggle Email Dataset Calculation, File Scanning, and Simple Risk Scoring |
Online Beykoz
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| 13) |
Email Forensics and Incident Reporting with AI Agent (Flowise + Ollama) |
Online Beykoz
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| 14) |
General Review, Final Project Presentations, and Cybersecurity Career Paths |
Online Beykoz
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Programme Learning Outcomes |
Contribution Level (from 1 to 5) |
| 1) |
It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results. |
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| 2) |
It explains the principles of algorithm design and develops software for solving problems using at least one programming language. |
5 |
| 3) |
It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data. |
3 |
| 4) |
Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations. |
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| 5) |
They apply natural language processing techniques to text data and develop basic NLP-based applications. |
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| 6) |
It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools. |
3 |
| 7) |
It creates data-driven decision models using decision support systems. |
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| 8) |
It develops optimization models and produces solutions for industrial and sectoral problems. |
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| 9) |
In professional practice, we operate within the framework of ethical principles, data security, and social responsibility. |
4 |
| 10) |
They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness. |
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| WORKLOAD OF TEACHING & LEARNING ACTIVITIES |
| Teaching & Learning Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Course |
14 |
1 |
14 |
| Laboratory |
14 |
2 |
28 |
| Application |
0 |
0 |
0 |
| Special Course Internship (Work Placement) |
0 |
0 |
0 |
| Field Work |
0 |
0 |
0 |
| Study Hours Out of Class |
0 |
0 |
0 |
| Presentations / Seminar |
0 |
0 |
0 |
| Project |
0 |
0 |
0 |
| Homework Assignments |
0 |
0 |
0 |
| Total Workload of Teaching & Learning Activities |
- |
- |
42 |
| WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES |
| Assesment & Evaluation Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Quizzes |
1 |
25 |
25 |
| Midterms |
1 |
35 |
35 |
| Semester Final Exam |
1 |
45 |
45 |
| Total Workload of Assesment & Evaluation Activities |
- |
- |
105 |
| TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) |
147 |
| ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) |
6 |