• Journal of Multimedia Information System
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• v.8 no.4
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• pp.251-258
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• 2021

In 2019, a novel coronavirus (COVID-19) outbreak started in China and spread all over the world. The countries went into lockdown and closed their borders to minimize the spread of the virus. Shortage of testing kits and trained clinicians, motivate researchers and computer scientists to look for ways to automatically diagnose the COVID-19 patient using X-ray and ease the burden on the healthcare system. In recent years, multiple frameworks are presented but most of them are trained on a very small dataset which makes clinicians adamant to use it. In this paper, we have presented a lightweight deep learning base automatic COVID-19 detection system. We trained our model on more than 22,000 dataset X-ray samples. The proposed model achieved an overall accuracy of 96.88% with a sensitivity of 91.55%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.49-52
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• 2019

머신러닝을 이용한 악성코드 분류 시스템의 대부분이 캐글 데이터셋 10,868건을 사용하여 분류의 정확도를 측정한다. 이 데이터셋에 포함된 바이러스 바이트코드에는 미확인(undefined)필드라는 부분이 과도하게 존재한다. 캐글 데이터셋 특정 Label의 미확인필드 포함도는 75%가 넘는 경우도 존재한다. 이 경우 미확인 필드를 어떻게 처리하느냐가 시스템의 성능에 가장 큰 영향을 끼친다. 본 연구에서는 이러한 캐글 데이터셋의 미확인필드 처리방법을 제시하고 그에 따른 분류 정확도를 연구하였다. 다양한 처리방법에 대한 정확도를 측정하여 제안한 방식의 타당성을 증명하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1611-1625
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• 2019

The emergence of new technologies and devices brings a new environment in the field of cyber security. It is not easy to predict possible security threats about new environment every time without special criteria. In other words, most malicious codes often reuse malicious code that has occurred in the past, such as bypassing detection from anti-virus or including additional functions. Therefore, we are predicting the security threats that can arise in a new environment based on the history of repeated malicious code. In this paper, we classify and define not only the internal information obtained from malicious code analysis but also the features that occur during infection and attack. We propose a method to predict and manage security threats in new environment by continuously managing and extending.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.365-373
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• 2021

Covid-19 posed a serious threat to public health worldwide, especially in the absence of vaccines or medicines. The only viable strategies to combat a virus with a high infection rate were to apply lock-down strategies, transport ban, social and physical distancing. In this work, we provide a domain-specific component model for crisis management. The model allows for building a plan for managing Covid-19 crisis and use the plan as a template to generate a system specific for managing that crisis. The crisis component model is derived from X-MAN II, a generic component model that we have developed for the aircraft industry

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.145-146
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• 2021

본 논문은 최근 이슈가 되고 있는 코로나19 시대에서 확진자 동선을 파악하기 위해 매장 등 시설에 방문했다는 기록을 남기는 과정 중 방문자가 QR 코드를 생성하고 관리자가 방문자의 QR 코드를 인식하는 방식과 반대로 방문자가 매장의 QR 코드를 직접 인식하게 하여 방문자와 매장 관리자가 겪을 수 있는 불편함을 덜어주기 위한 논문이다. App은 방문자와 매장 관리자 App이 따로 나눠져 있으며 사용자 App은 관리자의 QR을 스캔하여 방문기록을 남기고 관리자 App은 QR 코드를 생성만 하고 출입문에 비치하기만 하면 된다. Web도 관리자와 사용자로 나눠지는데 사용자는 자신의 방문기록과 감염 위험 경로 목록을 확인할 수 있으며 관리자는 매장에 다녀간 방문자의 목록과 확진자가 다녀갈 경우 감염 위험 경로 목록에 해당 사용자 정보가 나타나게 설계하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.647-650
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• 2021

The most powerful and modern face recognition techniques are using deep learning methods that have provided impressive performance. The outbreak of COVID-19 pneumonia has spread worldwide, and people have begun to wear a face mask to prevent the spread of the virus, which has led existing face recognition methods to fail to identify people. Mainly, it pushes masked face recognition has become one of the most challenging problems in the face recognition domain. However, deep learning methods require numerous data samples, and it is challenging to find benchmarks of masked face datasets available to the public. In this work, we develop a new simulated masked face dataset that we can use for masked face recognition tasks. To evaluate the usability of the proposed dataset, we also retrained the dataset with ArcFace based system, which is one the most popular state-of-the-art face recognition methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.539-541
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• 2023

COVID-19 is a viral pandemic disease that spreads widely all around the world. The only way to identify COVID-19 patients at an early stage is to stop the spread of the virus. Different approaches are used to diagnose, such as RT-PCR, Chest X-rays, and CT images. However, these are time-consuming and require a specialized lab. Therefore, there is a need to develop a time-efficient diagnosis method to detect COVID-19 patients. The proposed machine learning (ML) approach predicts the presence of coronavirus based on clinical symptoms. The clinical dataset is collected from the Israeli Ministry of Health. We used different ML classifiers (i.e., XGB, DT, RF, and NB) to diagnose COVID-19. Later, classifiers are optimized with the Bayesian hyperparameter optimization approach to improve the performance. The optimized RF outperformed the others and achieved an accuracy of 97.62% on the testing data that help the early diagnosis of COVID-19 patients.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.109-117
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• 2020

An edutainment system aims to help learners to recognize problems effectively, grasp and classify important information needed to solve the problems and convey the contents of what they have learned. Edutainment contents can be usefully applied to education and training in the both scientific and industrial areas. Our present work proposes an edutainment system that can be applied to a drug discovery process including virtual screening by using intuitive multi-modal interfaces. In this system, a stereoscopic monitor is used to make three-dimensional (3D) macro-molecular images, with supporting multi-modal interfaces to manipulate 3D models of molecular structures effectively. In this paper, our system can easily solve a docking simulation function, which is one of important virtual drug screening methods, by applying gaming factors. The level-up concept is implemented to realize a bio-game approach, in which the gaming factor depends on number of objects and users. The quality of the proposed system is evaluated with performance comparison in terms of a finishing time of a drug docking process to screen new inhibitors against target proteins of human immunodeficiency virus (HIV) in an e-drug discovery process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4512-4526
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• 2018

The Internet of Things (IoT) has become an emerging industry that is broadly used in many fields from industrial and agricultural manufacturing to home automation and hospitality industry. Because of the sheer number of connected devices transmitting valuable data, the IoT infrastructures have become a main target for cyber-criminals. One of the key challenges in protecting IoT devices is the lack of security measures by design. Although there are many hardware and software based security solutions (firewalls, honeypots, IPDS, anti-virus etc.) for information systems, most of these solutions cannot be applied to IoT devices because of the fact that IoT devices have limited computing resources (CPU, RAM,). In this paper, we propose a honeypot system called HoneyThing for modem/router devices (i.e. a kind of IoT device). HoneyThing emulates TR-069 protocol which is prevalent protocol used to remotely manage customer-premises equipment (CPE) devices, e.g. modems, routers. Honeything also serves an embedded web server simulating a few actual, critical vulnerabilities associated with the implementation of TR-069 protocol. To show effectiveness of the HoneyThing in capturing real world attacks, we have deployed it in the Internet. The obtained results are highly promising and facilitate to reveal network attacks targeting to CPE devices.

• Convergence Security Journal
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• v.8 no.4
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• pp.191-197
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• 2008

A safe computer environment is necessarily required for computer users, because of a damage is widely increased by a malicious software such as the warm, virus and trojan horse. A general vaccine program can detect after the malicious software intruded. This kinds of the vaccine program show good result against a malicious code which is well known, however, there is no function in the vaccine or not enough ability to detect an application software which a malicious code included. So, this paper proposes an application verification system to decide existence and nonexistence of a malicious code in the application software. The proposed application verification system with a mechanism that grasps the flow type of malicious code, can make a reduction of a damage for computer users before the application software executed.