• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.1-13
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• 2022

As the information and communication environment develops, the environment of military facilities is also development remarkably. In proportion to this, cyber threats are also increasing, and in particular, APT attacks, which are difficult to prevent with existing signature-based cyber defense systems, are frequently targeting military and national infrastructure. It is important to identify attack groups for appropriate response, but it is very difficult to identify them due to the nature of cyber attacks conducted in secret using methods such as anti-forensics. In the past, after an attack was detected, a security expert had to perform high-level analysis for a long time based on the large amount of evidence collected to get a clue about the attack group. To solve this problem, in this paper, we proposed an automation technique that can classify an attack group within a short time after detection. In case of APT attacks, compared to general cyber attacks, the number of attacks is small, there is not much known data, and it is designed to bypass signature-based cyber defense techniques. As an attack model, we used MITRE ATT&CK® which modeled many parts of cyber attacks. We design an impact score considering the versatility of the attack techniques and proposed a group similarity score based on this. Experimental results show that the proposed method classified the attack group with a 72.62% probability based on Top-5 accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.695-706
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• 2018

In recent years, industrial convergence centered on ICBM (internet of things (IoT), cloud, big data, mobile) has been experiencing rapid development in various fields such as agriculture and the financial industry. In order to prepare for cyber threats, one of the biggest problems facing the convergence industry in the future, the development of the industry must proceed in tandem with a framework of information security. In this study, we analyze the details of the current industrial development policy and related information protection policies using cross impact analysis and present policy priorities through the expert questionnaire. The aim of the study was to clarify the priorities and interrelationships within information security policy as a first step in suggesting effective policy direction. As a result, all six information security policy tasks derived from this study belong to key drivers. Considering the importance of policies, policies such as improving the constitution of the security industry and strengthening of support, training of information protection talent, and investing in the information security industry need to be implemented relatively first.

• The Journal of Society for e-Business Studies
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• v.20 no.3
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• pp.113-125
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• 2015

Information Security Incidents that have recently happen rapidly spread and the scale of that incidents' damage is large. In addition, as it proceeds to the era of converged industry in the future environment and the virtual cyber world expands to the physical world, new types of security threats have occurred. Now, it is time to supply security professionals who have a multi-dimensional security capabilities that can manage the strategies of technological security and physical security from the management point of view, rather than the ones who primarily focus on the traditional technologic-centered strategies to solve new types of security threats. In conclusion, in this paper we try to produce the curriculum of information security featured in the occupational classification system and analyze the subjects that are additionally required for those who move to other occupations to cultivate security professionals who suited to the converged-industrial environment. It is expected that multi-dimensional security professionals who suited to the converged-industrial environment will be cultivated by harmoniously integrating information security subjects from technological and business/managerial perspectives, and education training courses will be developed that effectively provide core knowledges per occupational classification when people moves to other occupations in the areas of information security.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3917-3941
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• 2019

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10d
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• pp.112-117
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• 2007

사이버 침해란 정보시스템의 취약한 부분을 공격하여 시스템 내부에 침입하거나 시스템을 마비/파괴하는 등의 사고를 유발하는 모든 행위를 말한다. 이러한 사이버 침해의 피해를 줄이기 위해 국내외 많은 연구 기관과 업체에서는 침입탐지시스템과 같은 정보보호 기술을 연구 개발하여 상용화하고 있다. 그러나 기존의 정보보호 기술은 이미 발생한 침해를 탐지하여 피해의 확산을 막는 데만 한정적으로 사용되고, 침해의 발생 가능성을 예측하지는 못하기 때문에 점차 첨단화, 다양화되고 있는 사이버 침해에 대응하기 힘들다는 문제점을 갖는다. 본 논문에서는 보안 취약점을 이용한 사이버 침해를 대상으로 전문가 설문을 통해 사이버 침해의 발생 가능성을 예측하는 방법을 제안하고, 이를 위한 사이버 침해 예측 항목을 추출하였다. 예측 항목 추출은 3 단계로 구성되며, 첫 번째 단계에서는 기존 연구와 사례 분석을 통해 예측 항목의 계층 구조를 생성한다. 두 번째 단계에서는 첫 번째 단계를 통해 생성된 예측 항목들을 델파이 방법을 통해 개선하여 최적의 예측 항목을 결정한다. 마지막 단계에서는 각 항목들에 대한 쌍대 비교 설문을 진행하여 항목 간 가중치를 추출한다.