• 정보보호학회논문지
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• 제33권3호
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• pp.577-589
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• 2023

국방부는 국방혁신 4.0 계획에 기반한 첨단과학기술군 육성을 위해 AI를 향후 전력 증강의 핵심 기술로 활용할 계획이다. 그러나 AI의 특성에 따른 보안 위협은 AI 기반의 국방정보시스템에 실질적인 위협이 될 수 있다. 이를 해소하기 위해서는 최초 개발 단계에서부터 체계적인 보안 활동의 수행을 통한 보안 내재화가 필요하다. 이에 본 논문에서는 AI 기반 국방정보시스템 개발 시 단계별로 수행해야 하는 보안 활동 수행 방안을 제안한다. 이를 통해 향후 국방 분야에 AI 기술 적용에 따른 보안 위협을 예방하고 국방정보시스템의 안전성과 신뢰성을 확보하는데 기여할 수 있을 것으로 기대한다.

• 산업경영시스템학회지
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• 제46권4호
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• pp.229-237
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• 2023

In this research, a new Test and Evaluation (T&E) procedure for defense AI systems is proposed to fill the existing gap in established methodologies. This proposed concept incorporates a data-based performance evaluation, allowing for independent assessment of AI model efficacy. It then follows with an on-site T&E using the actual AI system. The performance evaluation approach adopts the project promotion framework from the defense acquisition system, outlining 10 steps for R&D projects and 9 steps for procurement projects. This procedure was crafted after examining AI system testing standards and guidelines from both domestic and international civilian sectors. The validity of each step in the procedure was confirmed using real-world data. This study's findings aim to offer insightful guidance in defense T&E, particularly in developing robust T&E procedures for defense AI systems.

• 시스템엔지니어링학술지
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• 제19권1호
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• pp.91-106
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• 2023

Multidimensional efforts such as budgeting, organizing, and institutionalizing are being carried out for the adoption of defense AI. However, there is little interest in eliminating duplication of defense resources that may occur during the AI adoption. In this study, we propose a theoretical conceptual model to optimize duplication of AI technology that may occur during the AI adoption in the vast defense field. For a systematic approach, the JCA of the US DoD and system abstraction method are applied, and the IMO logical structure is used to decompose AI requirements and identify duplication. As a result of analyzing the effectiveness of our conceptual model through six example defense AI requirements, it was found that the amount of requirements of data and AI technologies could be reduced by up to 41.7% and 70%, respectively, and estimated costs could be reduced by up to 35.5%.

• 항공우주시스템공학회지
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• 제17권2호
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• pp.56-61
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• 2023

최근 국내·외 적으로 AI 기반의 MUM-T 복합전투체계 고도화와 이를 통한 산업의 활성화가 글로벌 이슈로 급부상하고 있다. 그러나 우리의 국방부 방위사업법은 무기체계 자체기기 중심으로 MUM-T 실증이 이루어져 무기체계의 운용 부분에 대한 고도화에 NATO 선도국 대비 다소 미흡한 것으로 판단된다. 우리의 미래 글로벌 UGV, UAV, UMS 등 MUM-T 복합전투체계의 국제경쟁력 강화를 위해서는 자율화 MUM-T 개념 정립, 자율화·상호운용성·데이터 표준화 기반의 국방 AI MUM-T 최상위 플랫폼 구축과 운용, 국가과학기술자문회의와 같은 대통령소속의 국방혁신위원회의 신설과 심의·자문 기능 강화·추가가 요구된다.

• 인터넷정보학회논문지
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• 제24권6호
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• pp.119-125
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• 2023

우크라이나-러시아 전을 통해 드론의 군사적 가치는 재평가되고 있으며, 북한은 '22년 말 대남 드론 도발을 통해 실제 검증까지 완료한 바 있다. 또한, 북한은 인공지능(AI) 기술의 드론 적용을 추진하고 있는 것으로 드러나 드론의 위협은 나날이 커지고 있다. 이에 우리 군은 드론작전사령부를 창설하고 다양한 드론 대응 체계를 도입하는 등 대 드론 체계 구축을 도모하고 있지만, 전력증강 노력이 타격체계 위주로 편중되어 군집드론 공격에 대한 효과적 대응이 우려된다. 특히, 도심에 인접한 공군 비행단은 민간 피해가 우려되어 재래식 방공무기의 사용 역시 극도로 제한되는 실정이다. 이에 본 연구에서는 AI기술이 적용된 적 군집드론의 위협으로부터 아 항공기의 생존성 향상을 위해 AI모델의 객체탐지 능력을 저해하는 소극방공 기법을 제안한다. 대표적인 적대적 머신러닝(Adversarial machine learning) 기술 중 하나인 적대적 예제(Adversarial example)를 레이저를 활용하여 항공기에 조사함으로써, 적 드론에 탑재된 객체인식 AI의 인식률 저하를 도모한다. 합성 이미지와 정밀 축소모형을 활용한 실험을 수행한 결과, 제안기법 적용 전 약 95%의 인식률을 보이는 객체인식 AI의 인식률을 제안기법 적용 후 0~15% 내외로 저하시키는 것을 확인하여 제안기법의 실효성을 검증하였다.

• 시스템엔지니어링학술지
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• 제19권2호
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• pp.135-144
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• 2023

Artificial Intelligence (AI) is a prominent topic in almost every field. In Korea, Systems Engineering (SE) procedures are applied in Defense Acquisition, and it is anticipated that SE procedures will also be applied to systems incorporating AI capabilities. This study explores the applicability of the concepts "AI4SE (AI for SE)" and "SE4AI (SE for AI)," which have been proposed in the United States, to the Korean context. The research examines the feasibility of applying these concepts, identifies necessary tasks, and proposes implementation strategies. For the AI4SE, many attempts and studies applying AI to SE Processes both Requirements & Architectures Define, System implementation & V&V, and Sustainment. It needs Explainability and Security. For the SE4AI, the Functional AI implementation level, Quality & Security of the Data-set, AI Ethics, and Review policies are needed. Furthermore, it provides perspectives on how these two concepts should ultimately converge and suggests future directions for development.

• 한국군사과학기술학회지
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• 제26권2호
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• pp.188-196
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• 2023

In the future battlefield centered on the concept of mosaic warfare, the need for an unmanned combat system will increase to value human life. It is necessary for Multiple/Heterogeneous Unmanned Combat Systems to have suitable mission planning method in order to perform various mission. In this paper, we propose the MTSR model for mission planning of the unmanned combat system, and introduce a method of identifying a task by a combination of services using a request operator and a method of allocating resources to perform a task using the requested service. In order to verify the performance of the proposed task-resource matchmaking algorithm, simulation using occupation scenarios is performed and the results are analyzed.

• 한국IT서비스학회지
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• 제20권1호
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• pp.29-40
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• 2021

North Korea's science and technology policies are being pursued under strong leadership and control by the central government. In particular, a large part of the research and development of science and technology related to the Fourth Industrial Revolution in North Korea is controlled and absorbed by the defense organizations under the national defense-oriented policy framework, among which North Korea is making national efforts to develop advanced technologies in artificial intelligence and actively utilize them in the military affairs. The future weapon system based on AI will have superior performance and destructive power that is different from modern weapons systems, which is likely to change the paradigm of the future battlefield, so a thorough analysis and prediction of the level of AI militarization technology, the direction of development, and AI-based weapons system in North Korea is needed. In addition, research and development of South Korea's corresponding weapon systems and military science and technology are strongly required as soon as possible. Therefore, in this paper, we will analyze the level of AI technology, the direction of AI militarization, and the AI-based weapons system in North Korea, and discuss the AI military technology and corresponding weapon systems that South Korea military must research and develop to counter the North Korea's. The next study will discuss the analysis of AI militarization technologies not only in North Korea but also in neighboring countries in Northeast Asia such as China and Russia, as well as AI weapon systems by battlefield function, detailed core technologies, and research and development measures.

• 한국군사과학기술학회지
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• 제25권6호
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• pp.637-647
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• 2022

Since the concept of Manned-UnManned Teaming(MUM-T) and Unmanned Aircraft System(UAS) can efficiently respond to rapidly changing battle space, many studies are being conducted as key components of the mosaic warfare environment. In this paper, we propose a rule-based AI engagement model based on Basic Fighter Maneuver(BFM) capable of Within-Visual-Range(WVR) air-to-air combat and a simulation environment in which human pilots can participate. In order to develop a rule-based AI engagement model that can pilot a fighter with a 6-DOF dynamics model, tactical manuals and human pilot experience were configured as knowledge specifications and modeled as a behavior tree structure. Based on this, we improved the shortcomings of existing air combat models. The proposed model not only showed a 100 % winning rate in engagement with human pilots, but also visualized decision-making processes such as tactical situations and maneuvering behaviors in real time. We expect that the results of this research will serve as a basis for development of various AI-based engagement models and simulators for human pilot training and embedded software test platform for fighter.

• 한국군사과학기술학회지
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• 제25권4호
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• pp.372-380
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• 2022

This study proposes a distributed parallel processing system, called the Fast Analysis System for remote sensing daTa(FAST), for large-scale satellite image processing and analysis. FAST is a system that designs jobs in vertices and sequences, and distributes and processes them simultaneously. FAST manages data based on the Hadoop Distributed File System, controls entire jobs based on Apache Spark, and performs tasks in parallel in multiple slave nodes based on a docker container design. FAST enables the high-performance processing of progressively accumulated large-volume satellite images. Because the unit task is performed based on Docker, it is possible to reuse existing source codes for designing and implementing unit tasks. Additionally, the system is robust against software/hardware faults. To prove the capability of the proposed system, we performed an experiment to generate the original satellite images as ortho-images, which is a pre-processing step for all image analyses. In the experiment, when FAST was configured with eight slave nodes, it was found that the processing of a satellite image took less than 30 sec. Through these results, we proved the suitability and practical applicability of the FAST design.