• Korean Security Journal
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• no.62
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• pp.347-367
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• 2020

Rapid advancement of technology in today's society has allowed for easy access and use of data, promoting the process of informationization. Along with the merits of such development, unintended consequences of security risks involving wiretapping have been increasing as well. The security threats posed by wiretapping technology must be addressed by every organization and individual, as it could be used to leak confidential information about the nation's security, military and diplomatic strategies, industrial technologies, and personal information. Despite increasing threats stemming from the surrounding nations using advanced wiretapping technology, there is a lack of awareness at the government level, and the existing security measures for detecting and counteracting the wiretapping equipment are ineffective. In this research, the authors offered technical suggestions for improving the security strategies against the threats of wiretapping and information leakage by conducting a content analysis. The authors suggested the units of an agency be assigned a security grade based on its importance, and that adequate security equipment should be operated according to the grade. For instance, around-the-clock surveillance is recommended for grade-1 facilities, and portable wiretapping equipment detectors should be used to protect conference rooms and other key sites.

• Review of KIISC
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• v.31 no.5
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• pp.47-54
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• 2021

사이버 보안 위협은 점차 은밀화·고도화되고 있어 단일 솔루션으로 탐지·분석이 어렵고, 파편화된 정보만으로 대응하는 데 한계가 있다. 이에 대응하기 위하여 글로벌 보안업체들은 CTA를 구성하여 신속 위협대응 체계를 구축하고 있다. 국내에서도 이에 발맞추어 다수의 보안 업체 및 기관들이 협업하여 2017년부터 3년간 Security Analytics 기반의 이기종 보안 솔루션 위협 분석 및 대응 기술 개발 과제를 수행하였다. 본 논문에서는 해당 과제의 수행성과 중 CTI 및 정보 공유 체계를 중심으로 정리하고 이를 통해 도출된 시사점과 현재 진행 방향을 정리하고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.561-562
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• 2017

Software defined networking technology allows centralized and powerful network control by separating packet processing and network control. However, powerfulness of software-defined networking technology threats the network itself. Most security researches of software-defined networking focus on discovering and defending network vulnerabilities. But, there is not much security for this technology itself. In this paper, the security vulnerabilities that can occur in this networking technology are analyzed and the security requirements of it are proposed. The biggest threats are the threats to the controller, the reliability problem between the controller and the switch must be solved, and a technique to detect attacks that malfunction by manipulating configuration information are needed.

• Journal of the Korea Society for Simulation
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• v.26 no.2
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• pp.51-58
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• 2017

This research focuses on convenient radar received power prediction method for detection predictions of North Korea SLBM(Submarine Launched Ballistic Missile). Recently, North Korea tested launching of SLBM which is threatening international security. Therefore, for active respondence to these threat, it is essential to analyze the radar detection prediction of SLBM. In this point of view, this work suggests a method for detection predictions for SLBM by simulating of RCS(Radar Cross Section) and wave propagation.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.584-586
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• 2018

사이버 공격 기법이 다양해지고 지능화됨에 따라 침해사고 발생이 증가하고 있으며, 그에 따른 피해도 확산되고 있다. 이에 따라 보안 기업들은 다양한 침해사고를 파악하고 빠르게 대처하기 위하여 위협정보를 정리한 인텔리전스 리포트를 배포하고 있다. 하지만 인텔리전스 리포트의 형식이 정형화되어 있지 않고 점점 증가하고 있어, 인텔리전스 리포트를 수작업을 통해 분류하기 힘들다는 문제점이 있다. 이와 같은 문제를 해결하기 위해 본 논문에서는 개체명 인식 시스템을 활용하여 비정형 인텔리전스 리포트에서 위협정보를 자동으로 탐지하고 추출할 수 있는 모델을 제안한다.

• Review of KIISC
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• v.23 no.6
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• pp.76-89
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• 2013

최근의 사이버 위협은 공격자에 의해 지속적이고 지능화된 위협으로 진화하고 있다. 이러한 위협은 장기간에 걸쳐 이루어지기 때문에 보안체계를 잘 갖추고 있는 회사라 하더라도 탐지하는데 한계가 있다. 본 논문에서는 차세대 보안관제 프레임워크의 지향점을 네트워크 가시성 강화, 상황인식 기반 지능형 보안관제, 관련 업무조직과의 정보 통합 및 협업 강화로 제시하고 있으며 구조적, 수집 파싱, 검색 분석, 이상 탐지 등 총 9개 관점에서 이를 지원하는 필요 기술들을 분류하였다. 아울러 침투 경로 및 공격 단계와 내부 자원 간 연관성 분석을 통한 수집 정보 범위 설정, 사례 기반 상관분석 규칙 생성 적용, 정보연동, 업무처리, 컴플라이언스, 조사 분석 등 지원 기능의 연계를 보안관제 모델링의 필요 요소로 도출하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.2025-2028
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• 2003

다양한 위협과 침입공격에 노출되어 있는 조직의 경우, 특정 제품에서 제공하는 한정된 침입탐지패턴의 한계를 극복하여 침입사건을 효과적으로 탐지하여 대응하기 위하여 이기종 침입탐지시스템 설치 및 운용이 요구된다. 이기종 침입탐지시스템 운용은 침입탐지 감사데이터 포맷이 제품별로 상이하고, 두개 제품 이상에 구현된 동일한 침입탐지 패턴이라도 설계의 차이점에 기인하여 오판률 가능성이 증가할 가능성이 있으며, 특히 탐지사건에 대한 대응으로 e-mail, SMS 등을 이용할 경우 중복 탐지로 인한 과도한 대응 등의 문제점이 있을 수 있으므로 이기종 침입탐지시스템 운영 환경에 적합한 기종간 통합 및 대응 모델과 관련 모듈 설계에 관한 연구가 필요하다 본 논문에서는 최근 연구되는 Aggregation 및 Correlation 개념을 적용하여 이기종 침입탐지시스템 운용 환경에서 침입탐지패턴 통합 및 대응을 위한 요구사항을 도출하고 통합 및 대응을 위한 IPMAC 모델 및 탐지알고리즘을 제시하여 관련 모듈을 설계 및 구현한 결과를 제안한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1841-1848
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• 2013

In this paper, the plans for improvement of real-time security monitoring accuracy and expansion of control region were investigated through comprehensive and systematic collection and analysis of the anomalous activities that inflow and outflow in the network on a large scale in order to overcome the existing security monitoring system based on stylized detection patterns which could correspond to only very limited cyber attacks. This study established an anomaly observation system to collect, store and analyze a diverse infringement threat information flowing into the darknet network, and presented the information classification system of cyber threats, unknown anomalies and high-risk anomalous activities through the statistics based trend analysis of hacking. If this security monitoring system utilizing darknet traffic as presented in the study is applied, it was indicated that detection of all infringement threats was increased by 12.6 percent compared with conventional case and 120 kinds of new type and varietal attacks that could not be detected in the past were detected.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.267-277
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• 2022

Due to the increasing proportion of cloud and remote working environments, various information security incidents are occurring. Insider threats have emerged as a major issue, with cases in which corporate insiders attempting to leak confidential data by accessing it remotely. In response, insider threat detection approaches based on machine learning have been developed. However, existing machine learning methods used to detect insider threats do not take biases and variances into account, which leads to limited performance. In this paper, boosting-type ensemble learning algorithms are applied to verify the performance of malicious insider detection, conduct a close analysis, and even consider the imbalance in datasets to determine the final result. Through experiments, we show that using ensemble learning achieves similar or higher accuracy to other existing malicious insider detection approaches while considering bias-variance tradeoff. The experimental results show that ensemble learning using bagging and boosting methods reached an accuracy of over 98%, which improves malicious insider detection performance by 5.62% compared to the average accuracy of single learning models used.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.411-425
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• 2023

Today, AI (Artificial Intelligence) technology is widely used in various fields, performing classification and regression tasks according to the purpose of use, and research is also actively progressing. Especially in the field of security, unexpected threats need to be detected, and unsupervised learning-based anomaly detection techniques that can detect threats without adding known threat information to the model training process are promising methods. However, most of the preceding studies that provide interpretability for AI judgments are designed for supervised learning, so it is difficult to apply them to unsupervised learning models with fundamentally different learning methods. In addition, previously researched vision-centered AI mechanism interpretation studies are not suitable for application to the security field that is not expressed in images. Therefore, In this paper, we use a technique that provides interpretability for detected anomalies by searching for and comparing optimization references, which are the source of intrusion attacks. In this paper, based on reference, we propose additional logic to search for data closest to real data. Based on real data, it aims to provide a more intuitive interpretation of anomalies and to promote effective use of an anomaly detection model in the security field.