For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ESW5038 | Digital Healthcare Security Laboratory 1 : Device Security | 2 | 4 | Major | Master/Doctor | - | No | ||
| Lab of digital healthcare for terminal is a main part of digital healthcare security. This course will cover any security breaches can be found in software in firmwares and OSes, network and hardware related digital healthcare. In the first part, students will experience learning breaches from know vulnerabilites and reproduce the breaches. Based on the experience, in the second part, students will detect new breaches by themselvles and design security patches for the founding. | |||||||||
| ESW5039 | Digital Healthcare Security Laboratory 2: Infrastructure Security | 2 | 4 | Major | Master/Doctor | - | No | ||
| In this lecture, we learn how to build secure infrastructures and ecosystems for digital healthcare. We particularly learn how to not only design and implement secure network infrastructure, database, protocols but also management and operations for administrators and operators in systems. | |||||||||
| ESW5040 | Digital Healthcare Security Laboratory 3 : Data Security | 2 | 4 | Major | Master/Doctor | 4 | - | No | |
| This course will cover various data analysis and machine learning methods to better understand and analyze the security and privacy issues in a digital health care area. | |||||||||
| ESW5041 | Digital Health Security | 3 | 6 | Major | Master/Doctor | 1-3 | - | No | |
| Digital THerapeutics sis one of the rapidly growing field which convergies ICT and Medical technologies. In this seminar, The cutting edge technologies such as IoT, AI, Display, will be presented and discussed. | |||||||||
| ESW5042 | Technical writing for software security | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This course is designed for graduate students to study the state-of-the-art software security work and write a paper about software security. | |||||||||
| ESW5043 | Modern Cryptography | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course is designed for graduate students to study the fundamental principles of modern cryptography and learn how to design secure cryptographic algorithms. | |||||||||
| ESW5044 | Program Analysis and Compiler Instrumentations for Software Security | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course explores program analysis and instrumentation techniques with a focus on literature study and analysis of their implementations. Software security has accumulated a plethora of compiler-based techniques for control-flow integrity and memory safety of programs. In this course, you will learn compiler concepts and techniques that are essential to systems security researchers through implementations that have been open-sourced or available in commodity compilers (e.g., LLVM). As a semseter-long project, you will implement your own proposal for a software security. | |||||||||
| ESW5045 | Design and Application of Trust Execution Technology | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course explores trusted execution technologies that serve as the foundation for secure computing in today’s computing devices. You will study cryptography concepts and the role of hardware security devices in today’s computing, as well as trusted execution environments such as Intel SGX and ARM Trustzone. As a semester-long project, you will propose and evaluate a research topic on TEEs. | |||||||||
| ESW5046 | Computer hardware systems for homomorphic encryption | 3 | 6 | Major | Master/Doctor | - | No | ||
| In the lecture, we study computer hardware systems that can acclerate homomorhpic encryption and applications that use such hardware accelerators. We study basic building blocks that constructs the accelerator hardware and design mechanisms that can optimize data transfers and computations. | |||||||||
| ESW5047 | Special topics in processor security | 3 | 6 | Major | Master/Doctor | - | No | ||
| In the lecture, we study security attacks that targets vulnarabilities in the moden computer processors. In addition, we also study the possible defense mechanisms against the security attacks and new comptuer architecture design methodologies that prevent possbile security attacks. | |||||||||
| ESW5048 | Trustworthy Machine Learning | 3 | 6 | Major | Master/Doctor | 1-4 | English | Yes | |
| As machine learning (ML) and deep learning (DL) systems are increasingly implemented in real-world applications to improve our lives, it is essential to guarantee that these systems exhibit appropriate and trustworthy behavior. Researchers and practitioners are increasingly interested in developing and deploying ML models and algorithms that are not just accurate, but also explainable, fair, privacy-preserving, causal, and robust. The course helps students learn about current efforts to develop trustworthy machine learning models. The course covers a variety of developing research issues relating to model fairness and transparency, ML/DL explainability, and security and privacy of ML models. The majority of the course contnent and readings will include of both seminal and recent publications in top venues. This course demands a solid understanding of machine learning, particularly deep learning and Python programming, in order to comprehend the course material. | |||||||||
| ESW5049 | Advanced Topics in Machine Learning | 3 | 6 | Major | Master/Doctor | English | Yes | ||
| This course is designed for graduate students who have a foundational understanding of machine learning and are looking to delve deeper into advanced concepts and recent ML-based applications. The course focus on recent advances in machine learning and goes in depth on selected topics and methods within machine learning and their applications, including generative models, probabilistic models and Bayesian methods, networks optimization algorithms, multimodal Learning, multitask learning, ensemble learning, attention mechanisms and transformers, interpretable models, graph neural networks, and adversarial learning. By covering these topics, the course also covers applications in domains such as computer vision, natural language processing, and biomedical. By completing this course, the student should be able to critically evaluate machine learning literature and current research trends. The course develops the ability to design, implement, and assess machine learning solutions for real-world applications. | |||||||||
| ESW5050 | Advanced Computer Vision Seminar | 3 | 6 | Major | Master/Doctor | English | Yes | ||
| o The purpose of this class is to get meaningful insight needed for student’s research by studying recent influential papers in the field of computer vision. o Each class includes presentation and discussion about 2~3 recent papers in the field of computer vision. - Papers are mostly recent(within 5 years) ML based computer vision methodologies(basic computer vision papers are not included, it means students taking this class should have enough knowledge to understand recent CV papers) - During a semester, students may study about 50 recent papers. o Paper list will be presented before a semester begin. o Student should choose papers to present in the semester and prepare the presentation about paper they chose. o After the presentation, discussion among students and wrap-up by lecturer. o Integrity and awareness of the presentation will be the key evaluation point of this class o If a student want, he or she can choose other recent and influential paper than papers in the list. | |||||||||
| ESW5051 | Advanced Cloud Security | 3 | 6 | Major | Master/Doctor | 1-4 | English | Yes | |
| This course explores the emerging security problems in the cloud regarding sensitive data computation through recent academic works. Modern cloud computing is facing a large attack vector with the potentially malicious service provider and the perils of side-channel attacks. Many recent works have proven the practicality of attacks that undermine the confidentiality and integrity of cloud computation. By learning about attack vectors and mitigations such as new access control mechanisms, the use of trusted hardware, and oblivious computing, this course provides a research-oriented perspective on the security issues in the cloud. | |||||||||
| ESW5052 | Data-Driven Security | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course focuses on the development of security services utilizing data. Students will explore privacy issues arising from the use of data and delve into security challenges associated with artificial intelligence and machine learning technologies. Through case studies and projects, students will develop strategies for data protection and privacy maintenance, identify vulnerabilities in AI/ML algorithms, and explore countermeasures. The course offers both theoretical knowledge and practical skills required in a data-centric security landscape. | |||||||||