• Korean Security Journal
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• no.56
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• pp.83-105
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• 2018

Special security guards play the role to safely operate and manage nuclear power plants from unintended internal and external threats. Physical fitness management of special security guards is one of the most important factors for improving and maintaining the level of physical protection. Thus, the purpose of this study is to analyze the physical fitness factor and physical fitness level required for mission accomplishment through job analysis of special security guards. The special security guards of Nuclear Power Plant in Korea were performing 7 jobs, 26 duties, 159 tasks. In order to accomplish theses tasks, the following physical fitness were required: muscle strength and muscle endurance of the hand, upper limb, lower limb and core, quickness, agility and Cardio function. The duties that require a lot of physical fitness were in the order of conducting arrest and self-defense, conducting unarmed defensive tactics, demonstrating proficiency with semiautomatic rifle, using protective equipment, performing emergency plan and defensive strategy, etc. The results of this study are expected to provide basic data necessary for establishing guidelines for fitness qualification and training of special security guards in the future and contribute to enhancement of physical protection of nuclear power plants.

• Korean Security Journal
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• no.57
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• pp.85-110
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• 2018

The bodyguard is continuously training basic physical training and security art martial to protect the privacy of security target, prepare and deal with external contingencies and threats. Currently, university students majoring in security are required to take a judo class, one of their security art martial, which can use a technology to catch, crush and repress opponent. Therefore, this study identified the effects of systematic training on body composition, Performance fitness, growth hormones, and IGF-1 among male university students through a 10-week judo training program so that it was committed to providing objective data to enhance the value of judo as a security art martial and as a result, we have a conclusion as follows: After 10 weeks of judo training, muscle mass increased significantly, and body fat rate and BMI decreased significantly. The muscular strength and power of Performance fitness were shown to increase significantly, and growth hormones were shown to increase significantly. In total, the above results showed that for judo training university students, overall body composition improved positively, the muscular strength and power of active physical fitness improved, and growth hormones increased. Thus, the increase in muscle strength and growth hormones through judo training will encourage fat breakdown due to the development of the body's muscles and increase bone density in the spine, thereby reducing the risk of fractures and preventing injury to the trainees who are performing a security art martial. It will also greatly help your health by preventing obesity, cardiovascular and metabolic diseases, which eventually will enhance your bodyguard function and prolong your life as a bodyguard.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5487-5495
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• 2012

The important issue that applies security key are secure rekeying, processing time and cost reduction. Because of sensor node's limited energy, energy consumption for rekeying affects lifetime of network. Thus it is necessary a secure and efficient security key management method. In this paper, I propose an energy efficient group-based cluster key management (EEGCK) in the large scale sensor networks. EEGCK uses five security key for efficient key management and different polynomial degree using security fitness function of sector, cluster and group is applied for rekeying and security processing. Through both analysis and simulation, I also show that proposed EEGCK is better than previous security management method at point of network energy efficiency.

• Convergence Security Journal
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• v.22 no.1
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• pp.135-140
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• 2022

This study aims to analyze exercise cases and issues using smart devices and technologies, and to present the development direction of a smart exercise environment suitable for the wellness life of active seniors with high activity and economic power unlike the existing silver generation. In the fitness industry, the subscription economy that regularly receives or uses necessary exercise tools, services, and digital content is expanding, and business models based on hardware sales and content subscription continue to emerge. In order to have value competitiveness as a platform that provides active seniors with integrated exercise services for health care, not only fitness centers, but also home training exercise equipment, fitness-related applications, and smart wearable device markets should be organically connected to form an expanded total platform. In order to have value competitiveness as a platform that provides active seniors with integrated exercise services for health care, not only fitness centers, but also home training exercise equipment, fitness-related applications, and smart wearable device markets should be organically connected to form an expanded total platform. The linkage of the digital healthcare function, which provides real-time changes to exercise programs based on continuous monitoring and feed back through wearable devices before, after, and during exercise by receiving and selecting exercise programs suitable for individual health status, is the differentiating factor in the smart fitness model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5865-5872
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• 2011

Multi-hop communication in clustering system is the technique that forms the cluster to aggregate the sensing data and transmit them to base station through midway cluster head. Cluster head around base station send more packet than that of far from base station. Because of this hot spot problem occurs and cluster head around base station increases energy consumption. In this paper, I propose a cluster group head selection using trajectory clustering technique(CHST). CHST select cluster head and group head using trajectory clustering technique and fitness function and it increases the energy efficiency. Hot spot problem can be solved by selection of cluster group with multi layer and balanced energy consumption using it's fitness function. I also show that proposed CHST is better than previous clustering method at the point of network energy efficiency.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.146-158
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• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.702-723
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• 2020

The application of machine learning (ML) in intrusion detection has attracted much attention with the rapid growth of information security threat. As an efficient multi-label classifier, kernel extreme learning machine (KELM) has been gradually used in intrusion detection system. However, the performance of KELM heavily relies on the kernel selection. In this paper, a novel multiple kernel extreme learning machine (MKELM) model combining the ReliefF with nature-inspired methods is proposed for intrusion detection. The MKELM is designed to estimate whether the attack is carried out and the ReliefF is used as a preprocessor of MKELM to select appropriate features. In addition, the nature-inspired methods whose fitness functions are defined based on the kernel alignment are employed to build the optimal composite kernel in the MKELM. The KDD99, NSL and Kyoto datasets are used to evaluate the performance of the model. The experimental results indicate that the optimal composite kernel function can be determined by using any heuristic optimization method, including PSO, GA, GWO, BA and DE. Since the filter-based feature selection method is combined with the multiple kernel learning approach independent of the classifier, the proposed model can have a good performance while saving a lot of training time.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.223-228
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• 2021

As a consequence of sudden outbreak of COVID-19 pandemic worldwide, educational institutes around the globe are forced to switch from traditional learning systems to e-learning systems. This has led to a variety of technology-driven pedagogies in e-teaching as well as e-learning. In order to take the best advantage, an appropriate understanding of the cognitive capability is of prime importance. This paper presents an intelligent cognitive maturity recognition system for confidence-based e-learning. We gather the data from actual test environment by involving a number of students and academicians to act as experts. Then a Genetic Programming based simulation and modeling is applied to generate a generalized classifier in the form of a mathematical expression. The simulation is derived towards an optimal space by carefully designed fitness function and assigning a range to each of the class labels. Experimental results validate that the proposed method yields comparative and superior results which makes it feasible to be used in real world scenarios.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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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.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.1-10
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• 2019

Since big-data text mining extracts many features and data, clustering and classification can result in high computational complexity and low reliability of the analysis results. In particular, a term document matrix obtained through text mining represents term-document features, but produces a sparse matrix. We designed an advanced genetic algorithm (GA) to extract features in text mining for detection model. Term frequency inverse document frequency (TF-IDF) is used to reflect the document-term relationships in feature extraction. Through a repetitive process, a predetermined number of features are selected. And, we used the sparsity score to improve the performance of detection model. If a spam mail data set has the high sparsity, detection model have low performance and is difficult to search the optimization detection model. In addition, we find a low sparsity model that have also high TF-IDF score by using s(F) where the numerator in fitness function. We also verified its performance by applying the proposed algorithm to text classification. As a result, we have found that our algorithm shows higher performance (speed and accuracy) in attack mail classification.