• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2034-2046
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• 2023

Industrial control systems in nuclear facilities are facing increasing cyber threats due to the widespread use of information and communication equipment. To implement cyber security programs effectively through the RG 5.71, it is necessary to quantitatively assess cyber risks. However, this can be challenging due to limited historical data on threats and customized Critical Digital Assets (CDAs) in nuclear facilities. Previous works have focused on identifying data flows, the assets where the data is stored and processed, which means that the methods are heavily biased towards information security concerns. Additionally, in nuclear facilities, cyber threats need to be analyzed from a safety perspective. In this study, we use the system theoretic process analysis to identify system-level threat scenarios that could violate safety constraints. Instead of quantifying the likelihood of exploiting vulnerabilities, we quantify Security Control Measures (SCMs) against the identified threat scenarios. We classify the system and CDAs into four consequence-based classes, as presented in NEI 13-10, to analyze the adversary impact on CDAs. This allows for the ranking of identified threat scenarios according to the quantified SCMs. The proposed framework enables stakeholders to more effectively and accurately rank cyber risks, as well as establish security and response strategies.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.179-191
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• 2024

With the advancement of modern technology, cyber-attacks are always rising. Specialized defense systems are needed to protect organizations against these threats. Malicious behavior in the network is discovered using security tools like intrusion detection systems (IDS), firewall, antimalware systems, security information and event management (SIEM). It aids in defending businesses from attacks. Delivering advance threat feeds for precise attack detection in intrusion detection systems is the role of cyber-threat intelligence (CTI) in the study is being presented. In this proposed work CTI feeds are utilized in the detection of assaults accurately in intrusion detection system. The ultimate objective is to identify the attacker behind the attack. Several data sets had been analyzed for attack detection. With the proposed study the ability to identify network attacks has improved by using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.38-52
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• 2024

The 5G is the 5th generation mobile network that provides enhanced mobile broadband, ultra-reliable & low latency communications, and massive machine-type communications. New services can be provided through multi-access edge computing, network function virtualization, and network slicing, which are key technologies in 5G mobile communication. However, these new technologies provide new attack paths and threats. In this paper, we analyzed the overall threats of 5G mobile communication through a literature review. First, defines 5G mobile communication, analyzes its features and technology architecture, and summarizes possible security issues. Addition, it presents security threats from the perspective of user devices, radio access network, multi-access edge computing, and core networks that constitute 5G mobile communication. After that, security requirements for threat factors were derived through literature analysis. The purpose of this study is to conduct a fundamental analysis to examine and assess the overall threat factors associated with 5G mobile communication. Through this, it will be possible to protect the information and assets of individuals and organizations that use 5G mobile communication technology, respond to various threat situations, and increase the overall level of 5G security.

• Strategy21
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• s.40
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• pp.82-114
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• 2016

The purpose of this article is to analyze the progress of North Korea's SLBM threat, and to assess the technological capacity and threat level of its SLBMs. Currently, North Korea has approximately 1000 ballistic missiles, such as the SCUD, Musudan, and Nodong, in stock. This article pays close attention to the background and strategical implication behind North Korea's obsession with developing SLBMs despite possessing sufficient means to launch provocations with its current arsenal of ground based ballistic missiles and conventional weapons. Based on the abovementioned analysis, this article will recommend possible response directions for the ROK Armed Forces to North Korea's SLBM threat. It is highly difficult to detect SLBMs due to its stealthy nature, as it is launched underwater after covert infiltration. North Korea's SLBM is considered a game changer in that even one SLBM can significantly change the strategic balance of North East Asia. North Korea's SLBM test launch in August has made a 500km flight, landing 80km inside the JADIZ (Japan Air Defense Identification Zone), and as such, it is assessed that North Korea already possesses underwater ejection and cold launch capabilities. The most realistic response to North Korea's imminent SLBM threat is bolstering anti-submarine capabilities. ROK Armed Forces need to upgrade its underwater kill-chain by modernizing and introducing new airborne anti-submarine assets and nuclear-powered submarines, among many options. Moreover, we should integrate SM-3 missiles with the Aegis Combat system that possess strong detection capabilities and flexibility, thereby establishing a sea-based Ballistic Missle Defense (BMD) system centered around the Aegis Combat System, as sea-based ballistic missile threats are best countered out in the seas. Finally, the capabilities gap that could arise as a result of budgetary concerns and timing of fielding new assets should be filled by establishing firm ROK-US-Japan combined defense posture.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.39-47
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• 2016

The purpose of this study is to investigate the smartwork use resistance and Non-Class-Related Behavior of attendees in university smartwork class with the perspective of Motivation-Threat-Ability. To do this, this study built a research model and examined how smartwork switching cost, threat and self-efficacy affect Non-Class-Related Behavior through smartwork use resistance. We also examined the relationship between self-efficacy and Non-Class-Related Behavior. The survey method was used for this paper, and data from a total of 80 university students were used for the analysis. And structural equation model was used to analyze the data. The results of this empirical study is summarized as followings. First, switching cost and threat have direct effects on the use resistance of smartwork services. Second, smartwork use resistance has a negative effect on Non-Class-Related Behavior but self-efficacy has a positive effect on it. Further, it will provide meaning suggestion point of the importance of use resistance motivations in establishing the use policy of smartwork services.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.2
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• pp.127-133
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• 2014

This paper proposes a determination method of waypoints to maximize the survivability of a UAV. Voronoi diagram which is used for the initial selection of waypoint candidates is the most widely used path planning technique to avoid the threat as far as possible when the location and strength of the threat are given. But if threat strength is different each other and flight path is constrained along with straight lines, Voronoi diagram has limitations in real applications. In this study, the initial waypoints obtained from Voronoi diagram are optimized considering the shape of each threat. Here, a waypoint is optimized while adjacent waypoints are fixed. By repeating this localized optimization until whole waypoints are converged, computation time for finding the best waypoints is greatly reduced.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.9
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• pp.689-699
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• 2013

During extreme ionospheric storms, anomalous ionospheric delays and gradients could cause potential integrity threats to users of GNSS (Global Navigation Satellite System) augmentation systems. GNSS augmentation ground facilities must monitor these ionospheric anomalies defined by a threat model and alarm the users of safely-of-life applications within time-to-alerts. Because the ionospheric anomaly threat model is developed using data collected from GNSS reference stations, the use of poor-quality data can degrade the performance of the threat model. As the total number of stations increases, the number of station with poor GNSS data quality also increases. This paper analyzes the quality of data collected from Korean GPS reference stations using comprehensive GNSS data quality check algorithms. The results show that the range of good and poor qualities varies noticeably for each quality parameter. Especially erroneous ionospheric delay and gradients estimates are produced due to poor quality data. The results obtained in this study should be a basis for determining GPS data quality criteria in the development of ionospheric threat models.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.231-237
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• 2017

Threat intelligences collected from cyber incident sharing system and security events collected from Security Information & Event Management system are analyzed and coped with expanding malicious code rapidly with the advent of big data. Analytical classification of the threat intelligence in cyber incidents requires various features of cyber observable. Therefore it is necessary to improve classification accuracy of the similarity by using multi-profile which is classified as the same features of cyber observables. We propose a multi-profile ensemble model performed similarity analysis on cyber incident of threat intelligence based on both attack types and cyber observables that can enhance the accuracy of the classification. We see a potential improvement of the cyber incident analysis system, which enhance the accuracy of the classification. Implementation of our suggested technique in a computer network offers the ability to classify and detect similar cyber incident of those not detected by other mechanisms.

• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1396-1405
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• 2020

Cyber threats such as forced personal information collection and distribution of malicious codes using malicious URLs continue to occur. In order to cope with such cyber threats, a security technologies that quickly detects malicious URLs and prevents damage are required. In a web environment, malicious URLs have various forms and are created and deleted from time to time, so there is a limit to the response as a method of detecting or filtering by signature matching. Recently, researches on detecting and predicting malicious URLs using machine learning techniques have been actively conducted. Existing studies have proposed various features and machine learning algorithms for predicting malicious URLs, but most of them are only suggesting specialized algorithms by supplementing features and preprocessing, so it is difficult to sufficiently reflect the strengths of various machine learning algorithms. In this paper, a system for predicting malicious URLs using multiple machine learning algorithms was proposed, and an experiment was performed to combine the prediction results of multiple machine learning models to increase the accuracy of predicting malicious URLs. Through experiments, it was proved that the combination of multiple models is useful in improving the prediction performance compared to a single model.

• Journal of dental hygiene science
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• v.17 no.5
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• pp.413-422
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• 2017

This study investigated the effect of violent experience on burnout among some dental hygienists. The study subjects were 242 dental hygienists working at dental clinics. Data were collected by self-administered questionnaire including information such as demographics, work-related characteristics, working environment, experience of violence, and burnout. The violence was classified as verbal violence, physical threat, or physical violence committed by dentists, patients, or caregivers. Descriptive analysis, t-test, ANOVA, correlation, and multiple linear regression analysis were performed to examine the factors associated with burnout. The levels of verbal violence, physical threat, and physical violence by dentists were $0.53{\pm}1.26$, $1.12{\pm}2.70$, and $0.04{\pm}0.42$, respectively. The levels of verbal violence, physical threat, and physical violence by patients and caregivers were $1.50{\pm}1.89$, $1.41{\pm}2.24$, and $0.24{\pm}1.38$, respectively. The score of burnout was $3.13{\pm}0.43$. Total violence, verbal violence, and physical violence by dentists were positively correlated with burnout. Total violence, verbal violence, and physical threat by patients and caregivers were positively correlated with burnout. In multiple linear regression analysis, the level of physical violence by dentists was positively associated with burnout of dental hygienists (${\beta}=0.95$, p=0.032). The levels of total physical violence (${\beta}=0.28$, p=0.002), verbal violence (${\beta}=0.15$, p<0.001), and physical threat (${\beta}=0.19$, p=0.009) by the patients or caregivers were positively associated with burnout of dental hygienists. This study examined the association between violence and burnout among dental hygienists. The level of violence showed positive correlation with burnout. Environment improvement to protect employees from violence and for management of employees who experienced workplace violence are needed to reduce the burnout.