For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ESW4020 | Introduction to information security | 3 | 6 | Major | Bachelor/Master |
4
1 |
- | No | |
| This course broadly covers fundamentals and principles of information security including varying security topics: system security, network security, software security, web application security and basic concepts of cryptograpy. Besides, a student must be able to understand symetric/asymetric cryptography, digital signature, and public key infrastructure (PKI). Note that the course aims to assist a student who does not have any background on security (e.g., those who do not take any security course during undergraduate study). | |||||||||
| ESW4021 | Machine learning techniques for security | 3 | 6 | Major | Bachelor/Master |
4
1 |
- | No | |
| This course aims to cover varying machine learning techniques that are often harnessed in the field of information security. We brush up with linear algebra, basic probability and statistics, followed by learning linear regression, logistic regression, support vector machine (SVM), decision tree, random forest, and a dimension reduction technique. The course covers artificial neural network, deep neural network (DNN or deep learning) that consists of input/hidden/output layers. Also, it deals with convolutional neural network (CNN) and recurrent neural network (RNN). Furthermore, the course covers advanced architectures including Attention, Transformer, and BERT. A student will be able 1) to understand a security paper based on these techniques, and 2) to perform a hands-on lab via an individual/team project. | |||||||||
| ESW4022 | Software Analysis for Security | 3 | 6 | Major | Bachelor/Master |
4
1 |
- | No | |
| The class teaches both foundation and practical aspects of automated software analysis, which is used by various research domains such as software engineering, program analysis, and security. Such techniques are also increasingly adopted in industries in order to automatically detect SW bugs and vulnerabilities before officially releasing the products. In this course, the foundation of automated software analysis techniques such as the theory of abstract interpretation, data-flow analysis, concolic testing, symbolic execution, fuzzing, and instrumentation will be introduced. Moreover, the recent research papers from top-tier conferences which utilizes such automated analysis techniques will be introduced in order to teach students how these techniques can be used to solve research problems. In addition, through assignments, students will design and implement practical software analysis tools that find bugs and verify software properties. After taking this course, students are able to: - Understand the foundation and practice of software analysis techniques, and - Able to detect SW bugs and vulnerabilities automatically, and - Could perform research on automated SW analysis | |||||||||
| ESW4023 | Software Hacking Lab | 3 | 6 | Major | Bachelor/Master | Korean | Yes | ||
| The course will teach binary reverse engineering, vulnerability analysis, exploit development, patching vulnerabilities, bug hunting, etc. After the course, students will have understanding in advanced software attack and defense techniques. | |||||||||
| ESW4024 | Introduction to Recommender Systems | 3 | 6 | Major | Bachelor/Master | 1-4 | - | No | |
| Recommendation systems aim to use the user's click/purchase history and the content information of items to predict the user's hidden preferences and to provide items that the user would like to prefer. The recommendation systems have been widely used in various domains, such as Web applications, online streaming services, and E-Commerce. This course covers the basic concepts and implementations of various recommender models. We deal with collaborative filtering (CF), which utilizes only user history, and content-based filtering (CBF), which utilizes the similarity between items. Specifically, CF models include conventional neighbor-based and model-based methods for linear and non-linear models using deep neural networks. We also investigate factorization machines and sequential-based recommender models. Furthermore, we implement various recommender models and evaluate them. | |||||||||
| ESW4025 | Artificial Intelligence Ethics | 3 | 6 | Major | Bachelor/Master | English | Yes | ||
| With the current development of artificial intelligence, we can meet them in various parts of society. However, artificial intelligence is an amoral that cannot make ethical judgments on its own. Therefore, we need to understand and solve various ethical problems caused by artificial intelligence. In this class, we will look at the ethical problems, causes, and solutions of artificial intelligence. First, we will briefly learn about artificial intelligence and then look at the ethical problems they have. They can be largely divided into data, algorithms, and applications, so we will look at each of them. We will then look at the causes of these problems. In addition, we will look at algorithms and examples that actually solve them based on the analyzed problems and causes. | |||||||||
| ESW4026 | Computer Networks and Artificial Intelligence | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This course aims at the improvement of the capability to apply Artificial Intelligence (AI) technology to the Internet where AI is one of core technologies in the 4th industrial revolution. For this aim, it explains the fundamental protocols and systems of computer networks and security, and the AI technology which can be applied to the computer networking technology. To prepare for 6G network era, it deals with Intent-Based Networking (IBN), Cloud-based Security Services, Intelligent Internet of Things, and Wireless Networking for autonomous vehicles. Especially, AI-based networking and security technologies are handled for 6G core networks. Also, this course teaches students the protocol layers such as data link layer, network layer, transport layer, and application layer. The contents of this course consist of introduction for 1 week, 6G network technologies for 5 weeks, and computer network layers for 8 weeks. As the expected benefits of this course, the students not only learn the foundational knowledge of computer networks and security, but also, for the 4th industrial revolution era, they can be educated up to SW-AI experts who can graft the technologies of the AI and Machine Learning (ML) to the computer networking field. | |||||||||
| ESW5010 | Advanced Operating Systems | 3 | 6 | Major | Master/Doctor | 1-4 | Korean | Yes | |
| This course introduces the concepts, architectures, and functions of operating systems, and deeply discusses some major functions of operating systems, such as file systems, process management, processor management, memory management, and I/O management at the kernel level. In detail, core mechanisms of each function of the Unix and Linux kernel are introduced and discussed. With this course, the students will get the practical capabilities in designing and improving the operating system functions. | |||||||||
| ESW5012 | Topics in Real-Time Systems for Software Platforms | 3 | 6 | Major | Master/Doctor | 1-4 | English | Yes | |
| This course studies classic real-time systems' theories, and then investigates the-state-of-the-art issues of real-time systems towards supporting software platforms. First, fundamental scheduling theories are covered, including scheduling for the basic real-time task model in uniprocessor/ multiprocessor/cluster platforms, as well as that for the fork-join model and synchronization. Based on the theoretical background, up-to-date papers for real-time systems are studied so as to support software platforms. | |||||||||
| ESW5014 | Advanced Topics in Computer Graphics | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course covers fundamental theories, advanced techniques, and practice in computer graphics. The theories covered in this course include images, geometry, modeling, transformation, projection, shading, texture mapping, ray tracing, global illumination, and special effects. The course also includes practical techniques to implement the theories using graphics processors. | |||||||||
| ESW5017 | Creative Software Design 1 | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| The graduate students research on the topic assigned by their advisors. The subject of this course is the purpose of solving the bottleneck technique for small and medium-sized firms based on ICBM(IoT, Cloud, Bigdata, Mobile) + ICT convergence. | |||||||||
| ESW5018 | Creative Software Design 2 | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| The graduate students research on the topic assigned by their advisors. The subject of this course is the purpose of solving the bottleneck technique for small and medium-sized firms based on ICBM(IoT, Cloud, Bigdata, Mobile) + ICT convergence. | |||||||||
| ESW5019 | Creative ICT Convergence 1 | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Performs research on a topic based on results of "Creative SW Design". The subject of this course is the purpose of solving the bottleneck technique for small and medium-sized firms based on ICBM(IoT, Cloud, Bigdata, Mobile) + ICT convergence. | |||||||||
| ESW5021 | Doctoral Independent Research 1 | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This is a graduate-level course for students pursuing a doctoral degree. In this course, students identify a research problem and perform independent research on the selected problem during the semester under the guidance of their advisors. | |||||||||
| ESW5022 | Doctoral Independent Research 2 | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This is a graduate-level course for students pursuing a doctoral degree. In this course, students identify a research problem and perform independent research on the selected problem during the semester under the guidance of their advisors. | |||||||||