• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.8
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• pp.641-653
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• 2013

Recent cyber attacks targeting control systems are getting sophisticated and intelligent notoriously. As the existing signature based detection techniques faced with their limitations, a whitelist model with security techniques is getting attention again. However, techniques that are being developed in a whitelist model used at the application level narrowly and cannot provide specific information about anomalism of various cases. In this paper, we classify abnormal cases that can occur in control systems of enterprises and propose a new whitelist model for detecting abnormal cases.

• Electric Engineers Magazine
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• v.265 no.9
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• pp.22-29
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• 2004

최근에는 설비의 이상징후를 포착함으로써 사고를 예지하고 치명적인 상태로 진전되기 전에 보완하는 이른바 예측 보전(또는 예지보전) 기술을 중심으로 하는 사고 예방 방향으로 변화되어 가고 있다. 이 예측보전기술은 기기의 상태를 정량적으로 파악하여 이상징후를 초기단계에서 검지하는 이상예지진단과 기기성능의 경년적인 변화에 착안한 노화진단 등을 중심으로 하고 있다. 이글에서는 변압기, 차단기, 단로기, 전력용 콘덴서, 피뢰기 등 수배전설비 진단 및 보수점검에 대해서 설명하고자 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.159-160
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• 2019

본 연구에서는 영상 내 도로의 형태와 영상 내 객체들의 속성을 실시간으로 자기 학습하고 영상 전체에서 나타난 객체와 각 도로 차선을 지나는 객체들의 이상 징후를 판별하기 위해 교통 CCTV 영상을 활용한다. 각 도로 구간을 촬영한 교통 영상에서 추출한 이동 객체 로그에서 영상 내 도로 형태와 영상 내 객체들의 속성을 통해 감시 공간을 학습하고 학습된 정상 프로파일 대비 각 차선을 지나는 객체들과 영상 내 객체들의 이상 상황을 실시간에 판별한다.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.1-8
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• 2021

In this paper, we propose the construction of an IOT that monitors foresters' physical abnormalities in real time, performs emergency measures, and provides alarms for natural disasters or heatstroke such as a nearby forest fire or landslide. Nodes provided to foresters include 6-axis sensors, temperature sensors, GPS, and LoRa, and transmit the measured data to the network server through the gateway using LoRa communication. The network server uses 6-axis sensor data to determine whether or not a forester has any signs of abnormal body, and performs emergency measures by tracking GPS location. After analyzing the temperature data, it provides an alarm when there is a possibility of heat stroke or when a forest fire or landslide occurs in the vicinity. In this paper, it was confirmed that the real-time detection of physical abnormalities of foresters and the establishment of disaster early warning IOT is possible by analyzing the data obtained by constructing a node and a gateway and constructing a network server.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.15-22
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• 2020

The 4th industrial revolution and the rapid change in the data environment revealed technical limitations in the existing relational database(RDB). As a new analysis method for unstructured data in all fields such as IDC/finance/insurance, interest in graph database(GDB) technology is increasing. The graph database is an efficient technique for expressing interlocked data and analyzing associations in a wide range of networks. This study extended the existing RDB to the GDB model and applied machine learning algorithms (pattern recognition, clustering, path distance, core extraction) to detect new abnormal signs. As a result of the performance analysis, it was confirmed that the performance of abnormal behavior(about 180 times or more) was greatly improved, and that it was possible to extract an abnormal symptom pattern after 5 steps that could not be analyzed by RDB.

• Korean Deer Journal
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• v.2 no.3 s.8
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• pp.60-67
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• 1997

• Journal of Internet of Things and Convergence
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• v.7 no.3
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• pp.69-74
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• 2021

With the recent development of biometric authentication technology, the user authentication techniques using biometric authentication are increasing. Various problems arised in certification techniques that use various existing methods such as ID/PW. Therefore, recently, a method of improving security by introducing biometric authentication as secondary authentication has been used. In this thesis, proposal of the user authentication system that can detect user identification and anomalies using ECGs that are extremely difficult to falsify through the electrical biometric signals from the heart among various biometric authentication devices is studied. The system detects user anomalies by comparing ECG data received from a wrist-mounted wearable device-type ECG measurement tool with identification and ECG data stored in blockchain form on the database and identifying the user's location through a beacon system.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.39-48
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• 2022

Cyberattacks around the world continue to increase, and their damage extends beyond government facilities and affects civilians. These issues emphasized the importance of developing a system that can identify and detect cyber anomalies early. As above, in order to effectively identify cyber anomalies, several studies have been conducted to learn BGP (Border Gateway Protocol) data through a machine learning model and identify them as anomalies. However, BGP data is unbalanced data in which abnormal data is less than normal data. This causes the model to have a learning biased result, reducing the reliability of the result. In addition, there is a limit in that security personnel cannot recognize the cyber situation as a typical result of machine learning in an actual cyber situation. Therefore, in this paper, we investigate BGP (Border Gateway Protocol) that keeps network records around the world and solve the problem of unbalanced data by using SMOTE. After that, assuming a cyber range situation, an autoencoder classifies cyber anomalies and visualizes the classified data. By learning the pattern of normal data, the performance of classifying abnormal data with 92.4% accuracy was derived, and the auxiliary index also showed 90% performance, ensuring reliability of the results. In addition, it is expected to be able to effectively defend against cyber attacks because it is possible to effectively recognize the situation by visualizing the congested cyber space.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.458-460
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• 2020

최근 여러 IoT 기기 사용이 급증하면서 보안 위협요소가 사회적인 문제로 대두되고 있다. 이러한 IoT 기기에서 사용자 생체정보를 활용해 사용자를 인증하는 시스템은 보편적으로 사용되고 있다. 그 중 지문인식은 스마트폰부터 여러 기기에 탑재되어 있다. 본 논문에서는 지문인식 센서에 온도, 압력, 터치 센서를 추가하는 기법과 군집화, 패턴 학습 알고리즘을 적용하여 위 변조 검출 능력을 향상시키는 방법을 통하여 지문인식에 대한 이상 징후 탐지 방법을 제안하고자 한다.

• Electronics and Telecommunications Trends
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• v.20 no.1 s.91
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• pp.22-32
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• 2005

기존의 사이버 공격은 특정 호스트나 서버를 목표로 하여 정보의 탈취 및 변경 등에 집중되었으나, 현재는 직접 혹은 간접적으로 과다 트래픽을 유발하여 네트워크 서비스를 마비시키는 방향으로 그 경향이변하고 있다. 이런 사이버 공격을 방지하여 네트워크의 안정적인 서비스의 제공을 위해서는 공격 징후나 이상 징후를 탐지하고 네트워크 차원에서 이에 대한 대응 방안을 결정하여 이를 네트워크 상에 강제할 수 있는 체계적인 보안 관리가 이루어져야 한다. 또한 네트워크 각 운용 주체별로 개별 보안 상황에 대해 적용할 보안 정책이 다르므로 이를 모델링하고 적용할 수 있는 방법이 제공되어야 한다. 본 논문에서는 정책 기반 네트워크 보안 관리 기능을 수행하기 위해 필요한 공격 및 이상 징후의 탐지, 그에 대한 대응과 이런 일련의 작업에 보안 정책을 강제하기 위한 보안 정책관련 연구 동향에 대해 다루도록한다.