• 디지털산업정보학회논문지
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• 제14권2호
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• pp.19-25
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• 2018

With the development of IT technology, various services such as artificial intelligence and autonomous vehicles are being introduced, and many changes are taking place in our lives. However, if secure security is not provided, it will cause many risks, so the information security becomes more important. In this paper, we analyzed the research trends of main themes of information security over time. In order to conduct the research, 'Information Security' was searched in the Web of Science database. Using the abstracts of theses published from 1991 to 2016, we derived main research topics through topic modeling and time series regression analysis. The topic modeling results showed that the research topics were Information technology, system access, attack, threat, risk management, network type, security management, security awareness, certification level, information protection organization, security policy, access control, personal information, security investment, computing environment, investment cost, system structure, authentication method, user behavior, encryption. The time series regression results indicated that all the topics were hot topics.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.271-281
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• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.

• 융합보안논문지
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• 제18권3호
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• pp.3-9
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• 2018

DDPG(Deep Deterministic Policy Gradient)알고리즘은 인공신경망과 강화학습을 사용하여 학습하는 알고리즘이다. 최근 많은 연구가 이루어지고 있는 강화학습과 관련된 연구 중에서도 DDPG 알고리즘은 오프폴리시로 학습하기 때문에 잘못된 행동이 누적되어 학습에 영향을 미치는 경우를 방지하는 장점이 있다. 본 연구에서는 DDPG 알고리즘을 응용하여 자전거를 자율주행 하도록 제어하는 실험을 진행하였다. 다양한 환경을 설정하여 시뮬레이션을 진행하였고 실험을 통해서 사용된 방법이 시뮬레이션 상에서 안정적으로 동작함을 보였다.

• 디지털산업정보학회논문지
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• 제18권4호
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• pp.41-46
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• 2022

In the fourth industrial era, when various technologies are fused and combined, new and advanced technologies from other industries are used extensively in the maritime industry field. New security threats are also increasing along with the development of new technologies. In addition, in incorporating convergence technologies into the maritime industry, various problems, such as communication definitions and procedures between technologies and customer-customized delays, occur. In this paper, for the problems mentioned above, research results on the network configuration of safer autonomous vessels by supplementing and fusing existing solutions rather than developing new technologies are proposed. In conclusion, the entire network consists of VDI and presents additional configurations to ensure confidentiality, integrity, and availability, which are the three security elements. According to the composition of such a convergence network, it is intended to help prepare countermeasures to protect internal data from external threats.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.139-144
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• 2022

Technological advancements taken the health care industry by a storm by embedding sensors in human body to measure their vitals. These smart solutions provide better and flexible health care to patients, and also easy monitoring for the medical practitioners. However, these innovative solutions provide their own set of challenges. The major challenge faced by embedding sensors in body is the issue of lack of infinite energy source. This work presents a meta-heuristic based routing model using modified PSO, and adopts an energy harvesting scheme to improve the network lifetime. The routing process is governed by modifying the fitness function of PSO to include charge, temperature and other vital factors required for node selection. A reactive routing model is adopted to ensure reliable packet delivery. Experiments have been performed and comparisons indicate that the proposed Energy Harvesting and Modified PSO (EHMP) model demonstrates low overhead, higher network lifetime and better network stability.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.285-291
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• 2022

WSN is the major component for information transfer in IoT environments. Source Location Privacy (SLP) has attracted attention in WSN environments. Effective SLP can avoid adversaries to backtrack and capture source nodes. This work presents a Two-Level Randomized Sector-based Routing (TLRSR) model to ensure SLP in wireless environments. Sector creation is the initial process, where the nodes in the network are grouped into defined sectors. The first level routing process identifies sector-based route to the destination node, which is performed by Ant Colony Optimization (ACO). The second level performs route extraction, which identifies the actual nodes for transmission. The route extraction is randomized and is performed using Simulated Annealing. This process is distributed between the nodes, hence ensures even charge depletion across the network. Randomized node selection process ensures SLP and also avoids depletion of certain specific nodes, resulting in increased network lifetime. Experiments and comparisons indicate faster route detection and optimal paths by the TLRSR model.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.97-102
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• 2022

Network intrusion detection is becoming an increasing necessity for both organizations and individuals alike. Detecting intrusions is one of the major components that aims to prevent information compromise. Automated systems have been put to use due to the voluminous nature of the domain. The major challenge for automated models is the noise and data imbalance components contained in the network transactions. This work proposes an ensemble model, Attribute Subset Selector Bagging (ASUB) that can be used to effectively handle noise and data imbalance. The proposed model performs attribute subset based bag creation, leading to reduction of the influence of the noise factor. The constructed bagging model is heterogeneous in nature, hence leading to effective imbalance handling. Experiments were conducted on the standard intrusion detection datasets KDD CUP 99, Koyoto 2006 and NSL KDD. Results show effective performances, showing the high performance of the model.

• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2018년도 공동국제학술발표회
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• pp.48-48
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• 2018

• 한국재난정보학회 논문집
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• 제19권3호
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• pp.481-489
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• 2023

연구목적: 이 연구는 자치경찰제도 도입에 따른 재난대응 실태를 살펴보고 개선방안을 제시하기 위한 목적의 연구이다. 연구방법: 연구의 완성을 위하여 경찰의 재난대응 매뉴얼과 관련 통계자료, 법령 등을 종합적으로 검토하여 개선하는 방안을 제시하였다. 연구결과: 자치경찰제도가 도입됨에 따라 각 지역의 실정에 맞도록 경찰을 운용하게 되었는데, 대형 재난 발생 시 대규모 인력이 동원 상황 발생 시 대응에 대하여 여전히 의문을 가지고 있다. 대형 대난 발생 시 자치경찰의 역할이 대하여 살펴본 결과, 현재 경찰이 대형재난 발생 시 대형재난 현장에서 2차 피해 예방을 위한 현장 통제 및 추가적인 재난 피해 예방, 요구호자 구조 등의 업무를 수행한다. 그러나, 자치경찰의 경우 대규모 재난에 대응하기가 쉽지않은 구조 탓에 대형재난에 취약할 수 밖에 없다. 결론:이 연구에서는 대형 재난 발생 시 기본적인 경찰의 재난현장 통제 및 2차 피해 예방활동과 함께 인근 지역 자치경찰과 협동 재난 대응 활동 및 컨트롤 타워 등 제안과 같은 방안을 제안하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권2호
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• pp.529-546
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• 2012

To address the need for autonomous control of remote and distributed mobile systems, Machine-to-Machine (M2M) communications are rapidly gaining attention from both academia and industry. M2M communications have recently been deployed in smart grid, home networking, health care, and vehicular networking environments. This paper focuses on M2M communications in the vehicular networking context and investigates areas where M2M principles can improve vehicular networking. Since connected vehicles are essentially a network of machines that are communicating, preferably autonomously, vehicular networks can benefit a lot from M2M communications support. The M2M paradigm enhances vehicular networking by supporting large-scale deployment of devices, cross-platform networking, autonomous monitoring and control, visualization of the system and measurements, and security. We also present some of the challenges that still need to be addressed to fully enable M2M support in the vehicular networking environment. Of these, component standardization and data security management are considered to be the most significant challenges.