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 |
Computer Science and Engineering | - | 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 |
Computer Science and Engineering | - | 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 |
Computer Science and Engineering | - | 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 | Computer Science and Engineering | 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 | Computer Science and Engineering | - | 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 | Computer Science and Engineering | 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 | Computer Science and Engineering | - | 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. | |||||||||
| ESW7001 | ICT Standard Technologies and Practice | 3 | 6 | Major | Bachelor/Master/Doctor | 1-4 | Computer Science and Engineering | Korean | Yes |
| This course aims at introducing ICT standard technologies to students, and letting them acquire the standard technologies by writing a standard draft. The ICT standard technologies include Artificial Intelligence (AI) and Networks, Smart Health, Satellite Communications, Internet of Things (IoT), Software-Defined Networking (SDN), Network Functions Virtualization (NFV), and Intent-Based Networking (IBN). It explains the introduction to ICT standardization, international standard technologies, detailed technologies per Standards Developing Organizations (SDOs), and international standardization strategies. The students can experience the process of developing a standard technology by writing a standard draft as a term project. The SDOs include 3GPP (3rd Generation Partnership Project) for the standardization of cellular networks and IETF (Internet Engineering Task Force) for the standardization of the Internet. | |||||||||
| ESW7002 | Data Modeling for Intelligent Networks and Security | 3 | 6 | Major | Bachelor/Master/Doctor | 1-4 | Computer Science and Engineering | English | Yes |
| This course aims at the teaching of Data Modeling and Management Automation for Intelligent Networks and Security. For various services in Software-Defined Networking (SDN) and Network Functions Virtualization (NFV), it explains YANG Data Modeling Language that has been developed by IETF (Internet Engineering Task Force) that is the Internet Standardization Organization. Remote control for management automation uses NETCONF and RESTCONF protocols. This course deals with YANG data models for the management automation of intelligent networks and security. Policies and rules on the networks and security can be constructed by the YANG data models that generate XML documents. These XML documents can be delivered to a server (i.e., network or security system) by a client (or administrator) to configure the policies and rules on the target server via NETCONF or RESTCONF. As an example for network security management automation, this course uses the framework and interfaces of Interface to Network Security Functions (I2NSF) in IETF. Through Intent-Based Networking (IBN), this course explains a security policy translator to automatically translate a high-level security policy into the low-level security policy for the requested network security services. | |||||||||
| GBA3035 | Integration of Systems and Business Informatics | 3 | 6 | Major | Bachelor | 3 | Global Business Administration | English | Yes |
| Topics to be covered include basic statistical concepts such as descriptive statistics, graphical representations of data, probabilities, probability distributions, and random variables. While investigating sampling distributions, estimation, inference, and basic simple linear regression analysis; many real world examples from various business and economic disciplines will be investigated for better business decisions. Applications and hands-on experiences will be emphasized by using Microsoft Excel program.(Pre-requisite: calculus) | |||||||||
| GEC3209 | Forecasting and Time Series Analysis Utilizing Big Data | 3 | 6 | Major | Bachelor | 2-4 | Global Economics | English | Yes |
| This class discusses econometric models and forecasting methods on economic and financial variables. Basic probability models and time series analysis are lectured and empirical studies are performed with real data. | |||||||||
| ICE2001 | Logic Circuits | 3 | 3 | Major | Bachelor | 2 | Information and Communication Engineering | English,Korean | Yes |
| Introductions in Boolean algebra, combinational logic circuits, and sequential logic circuits. Techniques to analyze and design digital logic circuits and systems are studied. Topics include Boolean algebra, logic minimization, multi-level combinational logic circuits, programmable and steering logic, flip-flops, timing issues, memory elements, and the basics of finite state machines. | |||||||||
| ICE2004 | Introduction to Information and Communication Engineering | 1 | 2 | Major | Bachelor | 1-2 | Information and Communication Engineering | - | No |
| In this subject, variuos branches of information and communication technology, such as communication, signal processing, semiconductor, computer, software, and power system are briefly introduced. This subject is provided to help the students orient themselves for their further studies in his/her college life. | |||||||||
| ICE2005 | Logic Design Laboratory | 2 | 4 | Major | Bachelor | 2-3 | Information and Communication Engineering | Korean,Korean | Yes |
| In this course, basic theories of digital system including the operation and characteristic of logic elements are reviewed. Students are encouraged to design and verify module blocks, i.e. adder, subtractor, encoder, decoder, multiplexer, flip-flop, synchronous and asynchronous counters, and shift registers. In addition to the basic logic circuit design, more complicated logic circuits such as ping-pong game, frequency counter, etc., are designed. FPGA design software and board are used for verifying VHDL-based digital circuit designs. Students are paticipated in design various combinational and sequential logic circuits using FPGA-based design kit. | |||||||||
| ICE2008 | Introduction to Electronic and Electrical Engineering | 2 | 4 | Major | Bachelor | 1 | Information and Communication Engineering | - | No |
| This course is intended to introduce students to the basic fundamentals of electrical and electronic engineering. Students will gain knowledge and understanding of basic DC and AC circuit theory, electronic devices, and components, and digital and analog electronics. This includes the engineering process, the technologies involved, the approach to problem solving, and the skills and tools used. | |||||||||