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Junho Choi
Division of Undeclared Majors, Chosun University, Gwangju 61452, Korea

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Journal article
Published: 07 April 2021 in Sustainability
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Recently, damages such as internal system intrusion, network and device vulnerability attacks, malicious code infection, and information leakage due to security attacks are increasing within the smart grid environment. Detailed and dynamic access control must be implemented to enable the power system in the smart grid environment to respond to such attacks. Dynamic and partial delegation must be available, and permission role restrictions must be considered for dynamic access control when delegating a role because of changes in power resource manager authority. In this paper, we propose an intelligent access control framework that can recognize security context by analyzing security vulnerabilities for security management of power systems. The intelligent access control framework is designed as a framework that enables collaboration within the smart grid environment, and a system administrator is designed to transmit access control policy information required between the power service principal and the agent. In addition, an experiment is conducted for the control inference of security context ontology-based access, attack detection inference of the security context awareness service, and the attack response of the intelligent integrated access control system. Experimental results show that the precision of security context ontology-based access control inference is 70%, and the attack response rate of integrated access control is 72.8%.

ACS Style

HyoungJu Kim; Junho Choi. Intelligent Access Control Design for Security Context Awareness in Smart Grid. Sustainability 2021, 13, 4124 .

AMA Style

HyoungJu Kim, Junho Choi. Intelligent Access Control Design for Security Context Awareness in Smart Grid. Sustainability. 2021; 13 (8):4124.

Chicago/Turabian Style

HyoungJu Kim; Junho Choi. 2021. "Intelligent Access Control Design for Security Context Awareness in Smart Grid." Sustainability 13, no. 8: 4124.

Conference paper
Published: 01 October 2020 in Proceedings of the 2nd International Conference on Data Engineering and Communication Technology
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We demonstrate a security threat of mouse data by differentiating the real mouse data from the dummy mouse data by deriving features to have high accuracy based on data science. Features appearing between the mouse coordinates input by the user are analyzed, and the feature is defined as a feature for machine learning models to derive a method of improving the accuracy. As a result, we found a feature where the distance between coordinates is concentrated in a specific range. When the distance is used as a feature, we verified that the mouse data is stolen more accurately.

ACS Style

Kyungroul Lee; Hoon Ko; HyoungJu Kim; Sun-Young Lee; Junho Choi. Practical Vulnerability Analysis of Mouse Data According to Offensive Security Based on Machine Learning. Proceedings of the 2nd International Conference on Data Engineering and Communication Technology 2020, 504 -510.

AMA Style

Kyungroul Lee, Hoon Ko, HyoungJu Kim, Sun-Young Lee, Junho Choi. Practical Vulnerability Analysis of Mouse Data According to Offensive Security Based on Machine Learning. Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. 2020; ():504-510.

Chicago/Turabian Style

Kyungroul Lee; Hoon Ko; HyoungJu Kim; Sun-Young Lee; Junho Choi. 2020. "Practical Vulnerability Analysis of Mouse Data According to Offensive Security Based on Machine Learning." Proceedings of the 2nd International Conference on Data Engineering and Communication Technology , no. : 504-510.

Journal article
Published: 19 August 2020 in Digital Communications and Networks
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To improve the Energy Efficiency (EE) and spectrum utilization of Cognitive Wireless Powered Networks (CWPNs), a combined spatial-temporal Energy Harvesting (EH) and relay selection scheme is proposed. In the proposed scheme, for protecting the Primary User (PU), a two-layer guard zone is set outside the PU based on the outage probability threshold of the PU. Moreover, to increase the energy of the CWPNs, the EH zone in the two-layer guard zone allows the Secondary Users (SUs) to spatially harvest energy from the Radio Frequency (RF) signals of temporally active PUs. To improve the utilization of the PU spectrum, the guard zone outside the EH zone allows for the constrained power transmission of SUs. Moreover, the relay selection transmission is designed in the transmission zone of the SU to improve the EE of the CWPNs. The outage probabilities of the SU and PU, in addition to the EE of the CWPNs, are derived. The results reveal that the setting of a two-layer guard zone can effectively reduce the outage probability of the PU and improve the EE of CWPNs. Furthermore, the relay selection transmission decreases the outage probabilities of the SUs.

ACS Style

Yuan Gao; Haixia He; Rongjun Tan; Junho Choi. Combined spatial-temporal energy harvesting and relay selection for cognitive wireless powered networks. Digital Communications and Networks 2020, 7, 201 -213.

AMA Style

Yuan Gao, Haixia He, Rongjun Tan, Junho Choi. Combined spatial-temporal energy harvesting and relay selection for cognitive wireless powered networks. Digital Communications and Networks. 2020; 7 (2):201-213.

Chicago/Turabian Style

Yuan Gao; Haixia He; Rongjun Tan; Junho Choi. 2020. "Combined spatial-temporal energy harvesting and relay selection for cognitive wireless powered networks." Digital Communications and Networks 7, no. 2: 201-213.

Journal article
Published: 08 August 2019 in IEEE Access
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For a variety of cyber-attacks occurring in a power IoT-Cloud environment, conventional security intrusion incident detection and response technologies typically use pattern- and behavior-based statistical methods. However, they cannot provide fundamental solutions for a security intrusion or attacks, which are becoming more intelligent and diverse as time passes. Therefore, an effective response method that can respond to security intrusions intelligently while using an access control technique based on ontology reasoning is required. This can be achieved by adopting a variety of intelligent reasoning technologies for security intrusion incidents of power systems, such as various reasoning technologies based on the ontology and semantic-web technologies being actively studied in the field of intelligent systems, and malicious code detection technologies based on an intelligent access control model, text mining, and natural language processing technologies. Accordingly, a security context ontology was modeled by analyzing the security vulnerabilities of a power system in a power IoT-Cloud environment, and security context inference rules were defined. Furthermore, this paper presents an appropriate power IoT-Cloud security service framework that can be used in a power IoT-Cloud environment. In addition, a security mechanism that can be efficiently operated in such an environment is implemented. In experiments conducted for this application, attack context scenarios that commonly occur were created using a smart meter as an example, which is an essential power system device. Inference rules were then composed for each attack stage to check the paths of attacks those that exploit the vulnerability of a smart meter system. As a result, it was confirmed that a high level attack detection results can be obtained based on the inference rules.

ACS Style

Chang Choi; Junho Choi. Ontology-Based Security Context Reasoning for Power IoT-Cloud Security Service. IEEE Access 2019, 7, 110510 -110517.

AMA Style

Chang Choi, Junho Choi. Ontology-Based Security Context Reasoning for Power IoT-Cloud Security Service. IEEE Access. 2019; 7 ():110510-110517.

Chicago/Turabian Style

Chang Choi; Junho Choi. 2019. "Ontology-Based Security Context Reasoning for Power IoT-Cloud Security Service." IEEE Access 7, no. : 110510-110517.

Journal article
Published: 21 March 2019 in Cognitive Systems Research
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Existing antivirus programs detect malicious code based on fixed signatures; therefore, they have limitations in detecting metamorphic malicious code that lacks signature information or possesses circumventing code inserted into it. Research on the methods for detecting this type of metamorphic malicious code primarily focuses on techniques that can detect code based on behavioral similarity to known malicious code. However, these techniques measure the degree of similarity with existing malicious code using API function call patterns. Therefore, they have certain disadvantages, such as low accuracy and large detection times. In this paper, we propose a method which can overcome the limitations of existing methods by using the FP-Growth algorithm, a data mining technique, and the Markov Logic Networks algorithm, a probabilistic inference method. To perform a comparative evaluation of the proposed method's malicious code behavior detection, we performed inference experiments using malicious code with an inserted code for random malicious behavior. We performed experiments to select optimal weights for each inference rule to improve our malicious code behavior inferences’ accuracy. The results of experiments, in which we performed a comparative evaluation with the General Bayesian Network, showed that the proposed method had an 8% higher classification performance.

ACS Style

Chang Choi; Christian Esposito; Mungyu Lee; Junho Choi. Metamorphic malicious code behavior detection using probabilistic inference methods. Cognitive Systems Research 2019, 56, 142 -150.

AMA Style

Chang Choi, Christian Esposito, Mungyu Lee, Junho Choi. Metamorphic malicious code behavior detection using probabilistic inference methods. Cognitive Systems Research. 2019; 56 ():142-150.

Chicago/Turabian Style

Chang Choi; Christian Esposito; Mungyu Lee; Junho Choi. 2019. "Metamorphic malicious code behavior detection using probabilistic inference methods." Cognitive Systems Research 56, no. : 142-150.

Special issue paper
Published: 10 January 2019 in Concurrency and Computation: Practice and Experience
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With the rapid development of China's social economy, the transportation needs of its residents continue to increase and encourage rapid and continual development of the Chinese high‐speed railway system. The design of China's high‐speed railway station is the integration of a new model that includes the layout of upcoming booking, traffic, waiting, tickets, and other functions to be set in the area known as the waiting hall. As such, the waiting hall has become an extremely important element of the environmental monitoring system. This paper proposes the design of a high‐speed rail station environmental monitoring system based on the LoRa communication technology. This system aims to enable the real‐time monitoring of temperature, humidity, illuminance, and noise decibels in high‐speed railway stations. The wireless communication present in the proposed system adopts the LoRa technology features of low power consumption, low cost, and long‐range communication. In this study, we set up a prototype of a LoRa enabled network in the lab building of Hohai University to transmit the environmental data. Moreover, we analyzed the signal propagation of LoRa in an indoor environment.

ACS Style

Wei Li; Guogang Liu; Junho Choi. Environmental monitoring system for intelligent stations. Concurrency and Computation: Practice and Experience 2019, 33, 1 .

AMA Style

Wei Li, Guogang Liu, Junho Choi. Environmental monitoring system for intelligent stations. Concurrency and Computation: Practice and Experience. 2019; 33 (2):1.

Chicago/Turabian Style

Wei Li; Guogang Liu; Junho Choi. 2019. "Environmental monitoring system for intelligent stations." Concurrency and Computation: Practice and Experience 33, no. 2: 1.

Conference paper
Published: 17 April 2016 in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Recently, a lot of users are increasing for searching travel information through smart devices such as, tourist attractions, accommodation, entertainment, local gourmet food and so on. A general method for recommendation system has a data sparseness and the first rate problem. This problem can be solved by ontology and inference rules. In this paper, we propose the travel destination recommendation using Markov Logic Networks based on probabilistic spatio-temporal inference. The most inference engines determine simply if there is a result from inference or not. However, probabilistic inference methods have emerged and classified problems that cannot be defined easily in the probabilistic way, which provides better results.

ACS Style

Chang Choi; Junho Choi; Htet Myet Lynn; Pankoo Kim. Travel Destination Recommendation Based on Probabilistic Spatio-temporal Inference. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016, 94 -100.

AMA Style

Chang Choi, Junho Choi, Htet Myet Lynn, Pankoo Kim. Travel Destination Recommendation Based on Probabilistic Spatio-temporal Inference. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2016; ():94-100.

Chicago/Turabian Style

Chang Choi; Junho Choi; Htet Myet Lynn; Pankoo Kim. 2016. "Travel Destination Recommendation Based on Probabilistic Spatio-temporal Inference." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 94-100.

Journal article
Published: 14 June 2015 in Pervasive and Mobile Computing
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The web services used on desktop can be accessed through a smartphone due to the development of smart devices. As the usage of smartphones increases, the importance of personal information security inside the smartphone is emphasized. The openness features of Android platform make a lot easier to develop an application and also deploying malicious codes into application is an easy task for hackers. The security practices are also growing rapidly as the number of malicious code increases exponentially. According to these circumstances, new methods for detecting and protecting the behavior of leaked personal information are needed to manage the personal information within a smartphone. In this paper, we study the permission access category in order to detect the malicious code, which discloses the personal information on Android environment such as equipment and location information, address book and messages, and solve the problem related to Resource access of Random Access Control method in conventional Android file system to detect the new malware or malicious code via the context ontology reasoning of permission access and API resource information which the personal information are leaked through. Then we propose an inference-based access control model, which can be enabled to access the proactive security. There is more improvement accuracy than existing malicious detecting techniques and effectiveness of access control model is verified through the proposal of inference-based access control model.

ACS Style

Junho Choi; Woon Sung; Chang Choi; Pankoo Kim. Personal information leakage detection method using the inference-based access control model on the Android platform. Pervasive and Mobile Computing 2015, 24, 138 -149.

AMA Style

Junho Choi, Woon Sung, Chang Choi, Pankoo Kim. Personal information leakage detection method using the inference-based access control model on the Android platform. Pervasive and Mobile Computing. 2015; 24 ():138-149.

Chicago/Turabian Style

Junho Choi; Woon Sung; Chang Choi; Pankoo Kim. 2015. "Personal information leakage detection method using the inference-based access control model on the Android platform." Pervasive and Mobile Computing 24, no. : 138-149.

Chapter
Published: 01 January 2015 in Econometrics for Financial Applications
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Most recommendation systems are based on historical data and profile files. The most common method is collaboration filtering. After analysis, a collaboration filtering recommender system differentiates one user profile from another to determine what to recommend. There has been substantial research on personalized social search. However, previous research has neglected semantic social information, making no use of definite relations between objects. This problem can be solved using ontology and inference rules. In this paper, Markov-logic-network (MLN)-based social relation inference is performed using social user information, such as country, age, and preference. In addition, this paper evaluates whether the inference results regarding social relations have been correctly predicted based on social user data. The user’s personal and business relations are inferred based on MLNs and a social network comprised of user profile data.

ACS Style

Junho Choi; Chang Choi; Eunji Lee; Pankoo Kim. Markov Logic Network Based Social Relation Inference for Personalized Social Search. Econometrics for Financial Applications 2015, 195 -202.

AMA Style

Junho Choi, Chang Choi, Eunji Lee, Pankoo Kim. Markov Logic Network Based Social Relation Inference for Personalized Social Search. Econometrics for Financial Applications. 2015; ():195-202.

Chicago/Turabian Style

Junho Choi; Chang Choi; Eunji Lee; Pankoo Kim. 2015. "Markov Logic Network Based Social Relation Inference for Personalized Social Search." Econometrics for Financial Applications , no. : 195-202.