For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ESW4013 | Automated Software Analysis | 3 | 6 | Major | Bachelor/Master | 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 | |||||||||
| ESW4014 | Principles of Reinforcement Learning | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | Korean | Yes | |
| In this course, students learn the basic theory algorithm of Reinforcement Learning (RL) to find the optimal policy for a given environment. From basic reinforcement learning theories such as Markov Decision Process, Planning, and Q-learning to deep neural network-based reinforcement algorithms such as Value Function Approximations and Policy Gradient Methods. In addition, Model-based RL through estimating environments, Exploitation & Exploration Trade-off, and Inverse RL that mimics the behavior of experts are also covered. Basic knowledge of data structures, algorithms and machine learning is required to take this course. | |||||||||
| ESW4015 | Network Security And Artificial Intelligence | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | - | No | |
| This course will explain the techniques of artificial intelligence (AI) and machine learning (ML) that are required by computer networks and security areas. First, it explains the basic mathematics and algorithms required by AI and ML.. Second, it explains representative AI techniques (e.g., genetic algorithm, evolutionary algorithms, artificial bee colony algorithm). Third, it explain representative ML techniques (e.g., decision tree, random forest, deep learning, reinforcement learning). Fourth, it teaches how to apply AI and ML techniques to computer networks and security areas with both journal and conference papers. The topics covered by this course include Internet of Things (IoT), vehicular networks, software-defined networking (SDN), network functions virtualization (NFV), 5G/6G, wireless drone networks, cloud-based security systems, and Blockchain. | |||||||||
| ESW4016 | Research Paper Writing In Network Security | 3 | 6 | Major | Bachelor/Master | 1 | Computer Science and Engineering | - | No |
| This course will explain research plan, design, implementation, performance evaluation, paper writing, preparation for paper presentation slides, presentation methology in computer networks and security areas. The topics covered by this course include Internet of Things (IoT), vehicular networks, software-defined networking (SDN), network functions virtualization (NFV), 5G/6G, wireless drone networks, cloud-based security systems, and Blockchain. | |||||||||
| ESW4017 | Seminar In Digital Healthcare Security | 1 | 2 | Major | Bachelor/Master | 1 | Computer Science and Engineering | Korean | Yes |
| This course will consist of seminars about the state-of-the-art terminal security, infrastructure security, and data security areas related to digital healthcare, which are delivered by domestic and international professors, researchers, and software engineers. The terminal security deals with security in system software and application software in various devices (e.g., smartphone, autonomous vehicle, and cloud server). The infrastructure security deals with security in various wired amd wireless networks (e.g., Internet of Things (IoT), vehicular networks, software-defined networking (SDN), network functions virtualization (NFV), 5G/6G, and Blockchain). The data security deals with security and privacy related to users’ data (e.g., digital healthcare data, video, photos, and audio). | |||||||||
| ESW4019 | Moving Object Networking and Security | 3 | 6 | Major | Bachelor/Master |
4
1 |
Computer Science and Engineering | - | No |
| This course aims at the teaching of Moving Object Networking and Security. Moving objects include mobile devices carried by humans, Internet-of-Things (IoT) devices with mobility, and Unmanned Aerial Vehicles (UAV), robots, and vehicles. That is, they mean terrestrial, aerial, and marine vehicles along with humans. To avoid collisions among those moving objects and let them collaborate with each other to achieve some missions, they need to communicate with each via one-hop or multi-hop wireless communications. This communications also need to be protected by security protocols. This course teaches students IPv6 wireless mobile networking and security of the moving objects as well as context-aware navigation for the safe movement of moving objects in terrestrial, aerial, and marine areas. | |||||||||
| 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. | |||||||||
| 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. | |||||||||
| ICE2003 | Probability and Random Processes | 3 | 6 | Major | Bachelor | 1-4 | Information and Communication Engineering | Korean,Korean | Yes |
| Introduction to basic probability theory and modeling random processes for the analysis and design of electric system. Topics may include axiomatic foundation of probability, conditional probability, indepedence, random variables, distribution function, density functions, characteristic functions, expectation and random process. | |||||||||