• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.140-147
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• 2010

The defense M&S (Modeling and Simulation) techniques are being used by the ROK Army for developing the efficiency and economics of national defense at operational level. But the problem we have found is the lack of interoperability and reusability in defense M&S. CMMS (Conceptual Model of the Mission Space) method have been used by militaries of some advanced countries for the purpose to solve these problems. Therefore, we have adapted the same approach in this paper as adapted by advanced countries military forces as named it as K-CMMS(Korean-Conceptual Model of the Mission Space). Considering the special ROK factors, we have suggested the framework of K-CMMS. We have tried to search for improving the reusability and interoperability in defense M&S.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1891-1908
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• 2021

Cyber deception defense mitigates Advanced Persistent Threats (APTs) with deploying deceptive entities, such as the Honeyfile. The Honeyfile distracts attackers from valuable digital documents and attracts unauthorized access by deliberately exposing fake content. The effectiveness of distraction and trap lies in the enticement of fake content. However, existing studies on the Honeyfile focus less on this perspective. In this work, we seek to improve the enticement of fake text content through enhancing its readability, indistinguishability, and believability. Hence, an enticing deceptive-content generator, EDGE, is presented. The EDGE is constructed with three steps: extracting key concepts with a semantics-aware K-means clustering algorithm, searching for candidate deceptive concepts within the Word2Vec model, and generating deceptive text content under the Integrated Readability Index (IR). Furthermore, the readability and believability performance analyses are undertaken. The experimental results show that EDGE generates indistinguishable deceptive text content without decreasing readability. In all, EDGE proves effective to generate enticing deceptive text content as deception defense against APTs.

• BMB Reports
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• v.55 no.7
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• pp.342-347
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• 2022

Defense priming allows plants to enhance their immune responses to subsequent pathogen challenges. Recent reports suggested that acquired resistances in parental generation can be inherited into descendants. Although epigenetic mechanisms are plausible tools enabling the transmission of information or phenotypic traits induced by environmental cues across generations, the mechanism for the transgenerational inheritance of defense priming in plants has yet to be elucidated. With the initial aim to elucidate an epigenetic mechanism for the defense priming in plants, we reassessed the transgenerational inheritance of plant defense, however, could not observe any evidence supporting it. By using the same dipping method with previous reports, Arabidopsis was exposed repeatedly to Pseudomonas syringae pv tomato DC3000 (Pst DC3000) during vegetative or reproductive stages. Irrespective of the developmental stages of parental plants that received pathogen infection, the descendants did not exhibit primed resistance phenotypes, defense marker gene (PR1) expression, or elevated histone acetylation within PR1 chromatin. In assays using the pressure-infiltration method for infection, we obtained the same results as above. Thus, our results suggest that the previous observations on the transgenerational inheritance of defense priming in plants should be more extensively and carefully reassessed.

• ETRI Journal
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• v.35 no.5
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• pp.748-756
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• 2013

This paper addresses the problem of online hop timing detection and frequency estimation of multiple frequency-hopping (FH) signals with antenna arrays. The problem is deemed as a dynamic one, as no information about the hop timing, pattern, or rate is known in advance, and the hop rate may change during the observation time. The technique of particle filtering is introduced to solve this dynamic problem, and real-time frequency and direction of arrival estimates of the FH signals can be obtained directly, while the hop timing is detected online according to the temporal autoregressive moving average process. The problem of network sorting is also addressed in this paper. Numerical examples are carried out to show the performance of the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.17-26
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• 2009

In national defense, established information systems are mainly based on simple information processing, such as mass data query. They have thus lacked intelligent ability of information and knowledge representation ability. We therefore need the research about the construction of military ontology which is the main topic for knowledge construction. Military ontology can help us develop the intelligent national defense information system which can search and manage information efficiently. In this paper, we present the six-step bottom-up approach for military ontology extraction, then we apply this approach to one of military domain, called national defense educational training, and finally implement it using $Prot\acute{e}g\acute{e}$ which is one of the most useful ontology development tool.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.189-198
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• 2024

AI is accepted not only in the private sector but also in the defense sector as a cutting-edge technology that must be introduced for the development of national defense. In particular, artificial intelligence has been selected as a key task in defense science and technology innovation, and the importance of data is increasing. As the national defense department shifts from a closed data policy to data sharing and activation, efforts are being made to secure high-quality data necessary for the development of national defense. In particular, we are promoting a review of the business budget system to secure data so that related procedures can be improved to reflect the unique characteristics of AI and big data, and research and development can begin with sufficient large quantities and high-quality data. However, there is a need to establish standardization and quality standards for structured data and unstructured data at the national defense level, but the defense department is still proposing standardization and quality standards for structured data, so this needs to be supplemented. In this paper, we propose an unstructured data set standard format for defense unstructured data sets, which are most needed in defense artificial intelligence, and based on this, we propose a standardization method for defense unstructured data sets.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.6
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• pp.865-876
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• 2018

This study focuses on the operating requirements of multi-resolution modeling(MRM) in training war-game model and proposes solutions for major issues of multi-resolution interoperation between Combat21 model and tank multi-purpose simulator(TMPS). We study the operating requirements of MRM through interviews with defense M&S experts and literature surveys and report the various issues that could occur with low-resolution model Combat21 and high-resolution model TMPS linked, for example, when to switch objects, what information to exchange, what format to switch to, and how to match data resolutions. This study also addresses the purpose and concept of training using multi-resolution interoperation, role of each model included in multi-resolution interoperation, and issue of matching damage assessments when interoperated between models with different resolutions. This study will provide the common goals and directions of MRM research to MRM researchers, defense modeling & simulation organizations and practitioners.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3712-3729
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• 2020

Smoke detection is helpful for early fire detection. With its large coverage area and low cost, vision-based smoke detection technology is the main research direction of outdoor smoke detection. We propose a two-stage smoke detection method combined with block Deep Normalization and Convolutional Neural Network (DNCNN) and visual change image. In the first stage, each suspected smoke region is detected from each frame of the images by using block DNCNN. According to the physical characteristics of smoke diffusion, a concept of visual change image is put forward in this paper, which is constructed by the video motion change state of the suspected smoke regions, and can describe the physical diffusion characteristics of smoke in the time and space domains. In the second stage, the Support Vector Machine (SVM) classifier is used to classify the Histogram of Oriented Gradients (HOG) features of visual change images of the suspected smoke regions, in this way to reduce the false alarm caused by the smoke-like objects such as cloud and fog. Simulation experiments are carried out on two public datasets of smoke. Results show that the accuracy and recall rate of smoke detection are high, and the false alarm rate is much lower than that of other comparison methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.201-220
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• 2020

Trackers, especially long-term (LT) trackers, now have a more complex structure and more intensive computation for nowadays' endless pursuit of high accuracy and robustness. However, computing efficiency of LT trackers cannot meet the real-time requirement in various real application scenarios. Considering heterogeneous CPU-GPU platforms have been more popular than ever, it is a challenge to exploit the computing capacity of heterogeneous platform to improve the efficiency of LT trackers for real-time requirement. This paper focuses on TLD, which is the first LT tracking framework, and proposes an efficient parallel implementation based on OpenCL. In this paper, we firstly make an analysis of the TLD tracker and then optimize the computing intensive kernels, including Fern Feature Extraction, Fern Classification, NCC Calculation, Overlaps Calculation, Positive and Negative Samples Extraction. Experimental results demonstrate that our efficient parallel TLD tracker outperforms the original TLD, achieving the 3.92 speedup on CPU and GPU. Moreover, the parallel TLD tracker can run 52.9 frames per second and meet the real-time requirement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.916-937
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• 2023

Most of the existing Distributed Denial-of-Service mitigation schemes in Software-Defined Networking are only implemented in the network domain managed by a single controller. In fact, the zombies for attackers to launch large-scale DDoS attacks are actually not in the same network domain. Therefore, abnormal traffic of DDoS attack will affect multiple paths and network domains. A single defense method is difficult to deal with large-scale DDoS attacks. The cooperative defense of multiple domains becomes an important means to effectively solve cross-domain DDoS attacks. We propose an efficient multi-domain DDoS cooperative defense mechanism by integrating blockchain and SDN architecture. It includes attack traceability, inter-domain information sharing and attack mitigation. In order to reduce the length of the marking path and shorten the traceability time, we propose an AS-level packet traceability method called ASPM. We propose an information sharing method across multiple domains based on blockchain and smart contract. It effectively solves the impact of DDoS illegal traffic on multiple domains. According to the traceability results, we designed a DDoS attack mitigation method by replacing the ACL list with the IP address black/gray list. The experimental results show that our ASPM traceability method requires less data packets, high traceability precision and low overhead. And blockchain-based inter-domain sharing scheme has low cost, high scalability and high security. Attack mitigation measures can prevent illegal data flow in a timely and efficient manner.