• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.147-155
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• 2024

Currently, the 4th Industrial Revolution, like other revolutions, is bringing great change and new life to humanity, and in particular, the demand for and use of drones, which can be applied by combining various technologies such as big data, artificial intelligence, and information and communications technology, is increasing. Recently, it has been widely used to carry out dangerous military operations and missions, such as the Russia-Ukraine war and North Korea's reconnaissance against South Korea, and as the demand for and use of drones increases, concerns about the safety and security of drones are growing. Currently, a variety of research is being conducted, such as detection of wireless communication abnormalities and sensor data abnormalities related to drones, but research on real-time detection of threats using radio frequency characteristic data is insufficient. Therefore, in this paper, we conduct a study to determine whether the characteristic data is normal or abnormal signal data by collecting radio frequency signal characteristic data generated while the drone communicates with the ground control system while performing a mission in a HITL(Hardware In The Loop) simulation environment similar to the real environment. proceeded. In addition, we propose an unsupervised learning-based threat detection system and optimal threshold that can detect threat signals in real time while a drone is performing a mission.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.1179-1189
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• 2019

Cyber security evaluation is a series of processes that estimate the level of risk of assets and systems through asset analysis, threat analysis and vulnerability analysis and apply appropriate security measures. In order to prepare for increasing cyber attacks, systematic cyber security evaluation is required. Various indicators for measuring cyber security level such as CWSS and CVSS have been developed, but the quantitative method to apply appropriate security measures according to the risk priority through the standardized security evaluation result is insufficient. It is needed that an Scoring system taking into consideration the characteristics of the target assets, the applied environment, and the impact on the assets. In this paper, we propose a quantitative risk assessment model based on the analysis of existing cyber security scoring system and a method for quantification of assessment factors to apply to the established model. The level of qualitative attribute elements required for cyber security evaluation is expressed as a value through security requirement weight by AHP, threat influence, and vulnerability element applying probability. It is expected that the standardized cyber security evaluation system will be established by supplementing the limitations of the quantitative method of applying the statistical data through the proposed method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.1
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• pp.103-111
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• 2009

Recently, cyber attacks against public communications networks are getting more complicated and varied. Moreover, in some cases, one country could make systematic attacks at a national level against another country to steal its confidential information and intellectual property. Therefore, the issue of cyber attacks is now regarded as a new major threat to national security. The conventional way of operating individual information security systems such as IDS and IPS may not be sufficient to cope with those attacks committed by highly-motivated attackers with significant resources. As a result, the monitoring and control of cyber security, which enables attack detection, analysis and response on a real-time basis has become of paramount importance. This paper discusses how to improve efficiency and effectiveness of national cyber security monitoring and control services. It first reviews major threats to the public communications network and how the responses to these threats are made and then it proposes a new approach to improve the national cyber security monitoring and control services.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.284-287
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• 2021

4차 산업혁명으로 인해 제조산업에 인공지능, 빅데이터와 같은 ICT 기술을 활용한 스마트팩토리의 제조 공정 자동화 및 장치 고도화 연구가 진행되고 있다. 제조 공정 자동화를 위해 스마트팩토리의 각 계층별 장치들이 유기적으로 연결되고 있으며, 이로 인해 발생 가능한 보안위협도 증가하고 있다. 스마트팩토리에서는 SIEM 등의 장비가 보안위협 데이터를 수집·분석·시각화하여 대응하고 있다. 보안위협 데이터 시각화에는 그리드 뷰, 피벗 뷰, 그래프, 차트, 테이블을 활용한 대시보드 형태로 제공하고 있지만, 이는 스마트팩토리 전 계층의 보안위협 데이터 확인에 대한 가시성이 부족하다. 따라서, 본 논문에서는 스마트팩토리 보안위협 데이터를 CVSS 점수 기반의 Likelihood와 보안위협 데이터 기반의 Impact를 활용하여 위험도를 도출하고, 히트맵 기반 스마트팩토리 보안위협 데이터 시각화 모델을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.206-207
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• 2023

최근 스마트워치는 통화, 문자, 간편 결제, 기타 장치 제어 등 스마트폰의 소형화 및 경량화 형태로 연구되어 여러 서비스를 제공하고 있다. 스마트워치는 스마트폰 대비 작은 물리적 크기로 인해 적용 가능한 하드웨어의 성능이 상대적으로 낮으며, 이로 인해 낮은 수준의 보안 기능을 제공한다. 이는 스마트워치 대상 보안위협으로 이어질 수 있으며, 이에 대응하기 위한 보안위협 분석 및 도출 연구가 필요한 실정이다. 따라서, 본 논문에서는 스마트워치의 하드웨어 적용 한계점으로 인한 스마트워치와 스마트폰의 성능 차이를 분석하고, 이로 인해 발생 가능한 보안위협을 도출한다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.644-647
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• 2004

Developing into information oriented society, dependency on network systems increases gradually in the area of economy, society and so on. Under that circumstance, temporary stop or damage of integrity of network system may have a strong impact on economic activities and overall society. Recently, threat of cyber terrorism, organized hostile action to government or industry through network, is increasing. Because of the features of network, it is not sufficient only a nation's countermeasure against Cyber terrorism, so international cooperation is needed. We review the status of countermeasure against cyber terrorism and examine several considerations for policy establishment.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.139-153
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• 2016

Cyber incident collected from cyber-threat-intelligence sharing Center is growing rapidly due to expanding malicious code. It is difficult for Incident analysts to extract and classify similar features due to Cyber Attacks. To solve these problems the existing Similarity Analysis Method is based on single or multiple cyber observable of similar incidents from Cyber Attacks data mining. This method reduce the workload for the analysis but still has a problem with enhancing the unreality caused by the provision of improper and ambiguous information. We propose a incident analysis model performed similarity analysis on the hierarchically classified cyber observable based on cyber incident that can enhance both availability by the provision of proper information. Appling specific cyber incident analysis model, we will develop a system which will actually perform and verify our suggested model.

• Convergence Security Journal
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• v.20 no.3
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• pp.115-123
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• 2020

Cyber Attack have evolved more and more in recent years. One of the best countermeasure to counter this advanced and sophisticated cyber threat is to predict cyber attacks in advance. It requires a lot of information and effort to predict cyber threats. If we use Open Source Intelligence(OSINT), the core of recent information acquisition, we can predict cyber threats more accurately. In order to predict cyber threats using OSINT, it is necessary to establish a Database(DB) for cyber attacks from OSINT and to select factors that can evaluate cyber threats from the established DB. We are based on previous researches that built a cyber attack DB using data mining and analyzed the importance of core factors among accumulated DG factors by AHP technique. In this research, we present a method for quantifying cyber threats and propose a cyber threats prediction model based on artificial neural networks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.3
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• pp.769-776
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• 2016

Recently, cyber attacks are continuously threatening the cyberspace of the state across the border. Such cyber attacks show a surface which is intelligent and sophisticated level that can paralyze key infrastructure in the country. It can be seen well in cases, such as hacking threat of nuclear power plant, 3.20 cyber terrorism. Especially in public institutions of the country in which there is important information of the country, advanced prevention is important because the large-scale damage is expected to such cyber attacks. Technical support is also important, but by improving the cyber security awareness and security expert knowledge through the cyber security education to the country's public institutions workers is important to raise the security level. This paper suggest education courses for the rise of the best security effect through a short-term course for the country's public institutions workers.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.2
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• pp.1-11
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• 2017

In this paper, we design and implement PADIL(Prediction And Detection of Information Leakage) system that predicts and detect information leakage behavior of insider by analyzing network traffic and applying a variety of machine learning methods. we defined the five-level information leakage model(Reconnaissance, Scanning, Access and Escalation, Exfiltration, Obfuscation) by referring to the cyber kill-chain model. In order to perform the machine learning for detecting information leakage, PADIL system extracts various features by analyzing the network traffic and extracts the behavioral features by comparing it with the personal profile information and extracts information leakage level features. We tested various machine learning methods and as a result, the DecisionTree algorithm showed excellent performance in information leakage detection and we showed that performance can be further improved by fine feature selection.