• 대한기계학회논문집A
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• 제27권8호
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• pp.1363-1370
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• 2003

In structural optimization, static loads are generally utilized although real external forces are dynamic. Dynamic loads have been considered in only small-scale problems. Recently, an algorithm for dynamic response optimization using transformation of dynamic loads into equivalent static loads has been proposed. The transformation is conducted to match the displacement fields from dynamic and static analyses. The algorithm can be applied to large-scale problems. However, the application has been limited to size optimization. The present study applies the algorithm to shape optimization. Because the number of degrees of freedom of finite element models is usually very large in shape optimization, it is difficult to conduct dynamic response optimization with the conventional methods that directly threat dynamic response in the time domain. The optimization process is carried out via interfacing an optimization system and an analysis system for structural dynamics. Various examples are solved to verify the algorithm. The results are compared to the results from static loads. It is found that the algorithm using static loads transformed from dynamic loads based on displacement is valid even for very large-scale problems such as shape optimization.

• International Journal of Aeronautical and Space Sciences
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• 제13권1호
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• pp.99-105
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• 2012

Track-before-detect techniques based on dynamic programming have provided solutions for detecting targets from a sequence of images. In its application to airborne threat detection, dynamic programming solutions should take into account the distinguishable properties of objects in a collision course. This paper describes the development of a new track scoring function that accumulates scores for airborne targets in Bayesian framework. Numerical results show that the proposed scoring function has slightly better detection capabilities.

• Structural Engineering and Mechanics
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• 제65권6호
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• pp.689-698
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• 2018

This study focuses on a novel procedure for the robustness assessment of reinforced concrete (RC) framed structures under threat-independent damage scenarios. The procedure is derived from coupled dynamic and non-linear static analyses. Two robustness indicators are defined and the method is applied to two RC frame buildings. The first building was designed for gravity load and earthquake resistance in accordance with Eurocode 8. The second was designed according to the tie force (TF) method, one of the design quantitative procedures for enhancing resistance to progressive collapse. In addition, in order to demonstrate the suitability and applicability of the TF method, the structural robustness and resistance to progressive collapse of the two designs is compared.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.98-100
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• 2023

IoT(Internet of Things) 기기를 활용하는 분야가 증가함에 따라 스마트 팩토리, 스마트 그리드 등 융합환경이 발전되었으며, 융합환경이 상호연결되는 IoBE(Internet of Things Blended Environment)가 조성되고 있다. 그러나, IoBE 구성요소가 복잡해짐에 따라 공격 표면이 증가하고, 기존에 알려진 보안위협이 융·복합되어 새로운 형태의 보안위협인 복합위협(BT, Blended Threat)이 발생할 수 있다. BT는 다양한 보안위협이 복합적으로 연계되어 발생함에 따라 예측하여 대응하기에 기존 보안위협보다 상대적으로 어려우며, 이에 대응방안 간의 조합을 통해 보안위협에 유동적으로 대응하는 동적 보안 프레임워크가 필요하다. 따라서, 본 논문에서는 BT에 대한 대응방안 동적 조합 프레임워크를 제안한다.

• 융합보안논문지
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• 제7권1호
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• pp.19-27
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• 2007

알려지지 않은 불분명한 위협을 검출하는 내는 것은 모순이다. 존재하는 것이 알려지지 않았다면 어떻게 찾아 낼 것인가? 그것은 알려지지 알은 위험을 아주 짧은 시간 안에 위협을 정의(identification)을 할 수 있으면 가능 할 수 있을 것이다. 본 논문은 위험 검출 기법을 만들어 네트워크상의 알려지지 않은 위험에 대해 유연하게 대처할 수 있는 시스템 개발에 도움을 줄 수 있게 하기 위해 연구되었다. 이 시스템은 알려지지 않은 위험을 탐지하기 위하여 동적이고 유연한 상황 지식을 가진 로직을 가지고 시스템을 감시한다. 시스템은 새로운 위협의 검출뿐만 아니라 빠르고 효과적인 방법으로 위협에 대처할 수 있다.

• 인터넷정보학회논문지
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• 제16권6호
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• pp.47-55
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• 2015

기존 동적 라우팅은 최소 링크 비용을 기준으로 최적의 라우팅 경로를 설정하고 이에 따라 유입 혹은 유출 되는 플로우를 단말로 전송한다. 하지만 이 경우에는 보안 기능을 담당하는 미들 박스를 우회하게 되고 이에 따라 해당 네트워크는 보안 위협을 직면하게 되는 문제가 발생한다. 따라서 최적의 라우팅 경로 설정 시 각 플로우의 서비스 유형을 고려하여 해당 플로우를 보안 미들 박스를 거쳐 단말로 전송하기 위한 라우팅 방안인 동적 서비스 체이닝이 필요하다. 본 논문에서는 SDN의 동적 플로우 트래픽 제어 기능과 보안 서비스 기능을 동시에 고려한 동적 라우팅 방안에 대해 제안한다.

• Journal of Information Processing Systems
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• 제16권4호
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• pp.882-895
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• 2020

The amount of malware increases exponentially every day and poses a threat to networks and operating systems. Most new malware is a variant of existing malware. It is difficult to deal with numerous malware variants since they bypass the existing signature-based malware detection method. Thus, research on automated methods of detecting and processing variant malware has been continuously conducted. This report proposes a method of extracting feature data from files and detecting malware using machine learning. Feature data were extracted from 7,000 malware and 3,000 benign files using static and dynamic malware analysis tools. A malware classification model was constructed using multiple DNN, XGBoost, and RandomForest layers and the performance was analyzed. The proposed method achieved up to 96.3% accuracy.

• Journal of Information Processing Systems
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• 제12권2호
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• pp.226-233
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• 2016

Cloud computing is a distributed computing model that has lot of drawbacks and faces difficulties. Many new innovative and emerging techniques take advantage of its features. In this paper, we explore the security threats to and Risk Assessments for cloud computing, attack mitigation frameworks, and the risk-based dynamic access control for cloud computing. Common security threats to cloud computing have been explored and these threats are addressed through acceptable measures via governance and effective risk management using a tailored Security Risk Approach. Most existing Threat and Risk Assessment (TRA) schemes for cloud services use a converse thinking approach to develop theoretical solutions for minimizing the risk of security breaches at a minimal cost. In our study, we propose an improved Attack-Defense Tree mechanism designated as iADTree, for solving the TRA problem in cloud computing environments.

• Strategy21
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• 통권42호
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• pp.83-113
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• 2017

After defeat in World War II, Japan's Peace Constitution committed the country to forego the acquisition of offensive military capabilities. However, in the midst of the post-cold war period, Japan began to change its security posture in line with the so-called 'normal state theory', which called for a more robust defense posture and expanded security activities. The second Abe administration promoted these security policies by issuing a National Security Strategy as well as a new National Defense Program Outline(NDPO) in 2013 and by establishing new security institutions such as the National Security Council. The Abe administration also adopted the new concept of a 'Unified Mobile Defense Force' in the 2013 which replaced the 'Dynamic Defense Force' as a new criteria for the Self-Defense Force's acquisition of military capabilities. In this new concept of military capabilities, the Ground Self-Defense Force is planning to replace existing divisions with mobile divisions and to form 'Amphibious Rapid Deployment Bridge' for the first time in 2018, which has long been taboo in Japan. Japan has experience a Marine Corps in the past. Likewise, an offensive changes in the military strategy can change the spectrum of strategy and 'Amphibious Rapid Deployment Bridge' plays a big role in this. Furthermore, Japan is increasing the Coast Guard's budget and capabilities in preparation for contingencies around the Senkaku islands (called the Diaoyu in Chinese). The South Korea navy should utilize Japan's changing security posture to deter immediate threat such as North Korea's military provocations and potential enemy threat such as China, Japan, Russia.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.177-189
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• 2023

Malware detection is an increasingly important operational focus in cyber security, particularly given the fast pace of such threats (e.g., new malware variants introduced every day). There has been great interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis. In this paper, we present a deep recurrent neural network solution as a stacked Long Short-Term Memory (LSTM) with a pre-training as a regularization method to avoid random network initialization. In our proposal, we use global and short dependencies of the inputs. With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting. The proposed method speeds up the convergence (in comparison to stacked LSTM) by reducing the length of malware OpCode or bytecode sequences. Hence, the complexity of our final method is reduced. This leads to better accuracy, higher Mattews Correlation Coefficients (MCC), and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time. Our proposed method can be applied in real-time malware threat hunting, particularly for safety critical systems such as eHealth or Internet of Military of Things where poor convergence of the model could lead to catastrophic consequences. We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis. For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method. More specifically, of our proposed method achieves an accuracy of 99.1% in detecting IoT malware samples, with AUC of 0.985, and MCC of 0.95; thus, outperforming standard LSTM based methods in these key metrics.