CSEC528 MACHINE LEARNING DESIGN AND APPLICATION FOR CYBER SECURITY
| Course Code: | 9100528 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (2.00 - 2.00) |
| ECTS Credit: | 8.0 |
| Department: | Cyber Security |
| Language of Instruction: | English |
| Level of Study: | Graduate |
| Course Coordinator: | |
| Offered Semester: | Fall and Spring Semesters. |
Course Objectives
The objective of this course is to give the fundamental concepts of artificial intelligence and introduce the students with setting up the coding environment, data manipulation and classification execution stages, different techniques of machine learning.
At the end of the course, the students are expected to learn different data mining techniques, coding in an interactive Python environment using artificial intelligence related libraries, executing different machine learning methods with a cyber security view.
Course Content
This course aims to familiarize the cyber security and information systems students with data mining techniques and machine learning methods, with hands-on demonstrations on different cyber security use cases. The course will be conducted by first discussing the related concepts in theoretical formal lectures then applying sample codes in practical laboratory sessions.
In the formal lectures, the concepts of artificial intelligence and data mining, such as data splitting & standardization, decision trees, linear & logistic regression, perceptron, support vector machines, naïve bayes, k-nearest neighbors, k-means, neural networks, self-organizing maps will be analyzed.
During the hands-on lab sessions, the discussed concepts & algorithms will be utilized in Python for different cyber security use cases. Several different Python libraries, code editing tools, and different datasets about phishing, virus signatures and network logs will be used. Using these datasets and discussed algorithms, sample tasks such as classification of anomalies, filtering spams, detecting malwares will be presented. The functions and methods in the employed libraries will also be discussed in the lab sessions.
Course Learning Outcomes
At the end of the course, the students are expected to learn different data mining techniques, coding in an interactive Python environment using artificial intelligence related libraries, executing different machine learning methods with a cyber security view.
Program Outcomes Matrix
| Level of Contribution | |||||
| # | Program Outcomes | 0 | 1 | 2 | 3 |
| 1 | leads and manages application and research projects related to the theoretical and methodological approaches on information security. | ✔ | |||
| 2 | knows fundamental concepts and techniques on enterprise information security management, information security management systems, ethics and information security governance. | ✔ | |||
| 3 | has in-depth and wide knowledge on cyber systems, operating systems and network security technologies, security risks including their analysis and risk mitigation techniques and methods. | ✔ | |||
| 4 | applies theoretical and methodical approaches of information security. | ✔ | |||
| 5 | has knowledge on analysis, design, implementation, and management of information systems requiring information security; uses related principles, methods, and tools. | ✔ | |||
| 6 | has deep and wide knowledge on cyber security and information security technology at a level to develop advanced applications. | ✔ | |||
| 7 | develops innovative solutions in the design of cyber security applications, processes, and systems. | ✔ | |||
| 8 | applies different quantitative and qualitative scientific research methods, has the ability to conduct scientific studies. | ✔ | |||
| 9 | conducts experimental and applied research. | ✔ | |||
| 10 | identifies the problems and the underlying causes in their area of expertise, develops methods to solve them, evaluates the results in line with objectives. | ✔ | |||
| 11 | follows academic literature on cyber security and information technologies; performs critical analysis of the information. | ✔ | |||
| 12 | works effectively both independently and in multi-disciplinary teams as a team member or leader. | ✔ | |||
| 13 | pays attention to ethical values and applies them at her professional and scientific studies. | ✔ | |||
| 14 | presents the recent developments in the field and their work, by supporting with qualitative and quantitative data effectively in written and spoken English and Turkish. | ✔ | |||
0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution