• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.471-476
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• 2011

As information and communication technologies (ICTs) has been developed, it is getting more important for internet users to consider many security issues (e.g., privacy protection). In this paper we claims that animation contents make a positive influence on teaching such computer securities, and investigate what features of animation contents will be the most important relationships with performance of the teaching process. Once we have obtained the survey results from students, two main features (i.e., characters and plots) of the animation contents have positively influenced the performance of e-learning systems.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1433-1447
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• 2022

Recently, as the demand for physical security technology to prevent leakage of technical and business information of companies and public institutions increases, the high tech companies are operating X-ray security checkpoints at building entrances to protect their intellectual property and technology. X-ray security checkpoints are operated to detect cameras and storage media that may store or leak important technologies in the bags of people entering and leaving the building. In this study, we propose an X-ray security checkpoint system that automatically detects a storage medium in an X-ray image using a deep learning based object detection method. The proposed system consists of an edge computing unit and a cloud-computing unit. We employ the RetinaNet for automatic storage media detection in the X-ray security checkpoint images. The proposed approach achieved mAP of 95.92% on private dataset.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.151-153
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• 2023

Federated learning (FL)-based network intrusion detection techniques have enormous potential for securing the Industrial Internet of Things (IIoT) cybersecurity. The openness and connection of systems in smart industrial facilities can be targeted and manipulated by malicious actors, which emphasizes the significance of cybersecurity. The conventional centralized technique's drawbacks, including excessive latency, a congested network, and privacy leaks, are all addressed by the FL method. In addition, the rich data enables the training of models while combining private data from numerous participants. This research aims to create an FL-based architecture to improve cybersecurity and intrusion detection in IoT networks. In order to assess the effectiveness of the suggested approach, we have utilized well-known cybersecurity datasets along with centralized and federated machine learning models.

• ETRI Journal
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• v.45 no.6
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• pp.1007-1021
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• 2023

Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795 000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders, for example, strokes, can minimize the disabling effects. Thus, in this paper, we consider various risk factors that contribute to the occurrence of stoke and machine learning algorithms, for example, the decision tree, random forest, and naive Bayes algorithms, on patient characteristics survey data to achieve high prediction accuracy. We also consider the semisupervised self-training technique to predict the risk of stroke. We then consider the near-miss undersampling technique, which can select only instances in larger classes with the smaller class instances. Experimental results demonstrate that the proposed method obtains an accuracy of approximately 98.83% at low cost, which is significantly higher and more reliable compared with the compared techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1462-1477
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• 2024

With the evolving complexity of connected vehicle features, the volume and diversity of data generated during driving continue to escalate. Enabling data sharing among interconnected vehicles holds promise for improving users' driving experiences and alleviating traffic congestion. Yet, the unintentional disclosure of users' private information through data sharing poses a risk, potentially compromising the interests of vehicle users and, in certain cases, endangering driving safety. Federated learning (FL) is a newly emerged distributed machine learning paradigm, which is expected to play a prominent role for privacy-preserving learning in autonomous vehicles. While FL holds significant potential to enhance the architecture of the Internet of Vehicles (IoV), the dynamic mobility of vehicles poses a considerable challenge to integrating FL with vehicular networks. In this paper, a novel clustered FL framework is proposed which is efficient for reducing communication and protecting data privacy. By assessing the similarity among feature vectors, vehicles are categorized into distinct clusters. An optimal vehicle is elected as the cluster head, which enhances the efficiency of personalized data processing and model training while reducing communication overhead. Simultaneously, the Local Differential Privacy (LDP) mechanism is incorporated during local training to safeguard vehicle privacy. The simulation results obtained from the 20newsgroups dataset and the MNIST dataset validate the effectiveness of the proposed scheme, indicating that the proposed scheme can ensure data privacy effectively while reducing communication overhead.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.477-491
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• 2010

This paper aimed to understand current trends in online lectures for Korean SAT based on students' academic performances through a qualitative interview approach. In results, the highest academic performance group showed a good deal of interest and usage in online lectures. This group participants preferred private online lectures to public ones. However, the lowest academic group liked to use public online lectures. The middle academic groups who lived in an expensive area spent a lot of money for tutoring or attending private institutes rather than online lectures. Suggestions are: self-regulated learning is needed, public onilne lectures should have improved contents, and lastly, these online lectures are required to be connected with regular school curriculum.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.4
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• pp.7-17
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• 2019

Machine learning is one of the most accurate techniques to predict and analyze various phenomena. K-means clustering is a kind of machine learning technique that classifies given data into clusters of similar data. Because it is desirable to perform an analysis based on a lot of data for better performance, K-means clustering can be performed in a model with a server that calculates the centroids of the clusters, and a number of clients that provide data to server. However, this model has the problem that if the clients' data are associated with private information, the server can infringe clients' privacy. In this paper, to solve this problem in a model with a number of clients, we propose a privacy-preserving K-means clustering method that can perform machine learning, concealing private information using homomorphic encryption.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1059-1068
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• 2022

A Post-Quantum Cryptographic algorithm NTRU, which is designed by considering the computational power of quantum computers, satisfies the mathematically security level. However, it should consider the characteristics of side-channel attacks such as power analysis attacks in hardware implementation. In this paper, we verify that the private key can be recovered by analyzing the power signal generated during the decryption process of NTRU. To recover the private keys, the Simple Power Analysis (SPA), Correlation Power Analysis (CPA) and Differential Deep Learning Analysis (DDLA) were all applicable. There is a shuffling technique as a basic countermeasure to counter such a power side-channel attack. Neverthe less, we propose a more effective method. The proposed method can prevent CPA and DDLA attacks by preventing leakage of power information for multiplication operations by only performing addition after accumulating each coefficient, rather than performing accumulation after multiplication for each index.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.1
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• pp.33-43
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• 2023

The National Institute of Standards and Technology (NIST), which is working on the Post-Quantum Cryptography (PQC) standardization project, announced four algorithms that have been finalized for standardization. In this paper, we demonstrate through experiments that private keys can be exposed by Correlation Power Analysis (CPA) and Differential Deep Learning Analysis (DDLA) attacks on polynomial coefficient-wise multiplication algorithms that operate in the process of generating signatures using CRYSTALS-Dilithium algorithm. As a result of the experiment on ARM-Cortex-M4, we succeeded in recovering the private key coefficient using CPA or DDLA attacks. In particular, when StandardScaler preprocessing and continuous wavelet transform applied power traces were used in the DDLA attack, the minimum number of power traces required for attacks is reduced and the Normalized Maximum Margines (NMM) value increased by about 3 times. Conseqently, the proposed methods significantly improves the attack performance.

• Korean Security Journal
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• no.33
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• pp.197-228
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• 2012

The private security industry in Korea has rapidly proliferated. While the industry has grown quickly, though, private security officers have recently been implicated in incidents involving violence, demonstrating an urgent need for systematic reform and regulation of private security practices in Korea. Due to its quasi-public service character, the industry also risks losing the public's favor if it is not quickly disciplined and brought under legitimate government regulation: the industry needs professional standards for conduct and qualification for employment of security officers. This paper shares insights for the reform of the Korean private security industry through a study of the licensing and training requirements for private security businesses in the United States, mainly focusing on the Private Security Officer Employment Authorization Act (hereinafter the PSOEAA) and the California system. According to the PSOEAA, aspiring security officers shall submit to a criminal background check (a check of the applicants' criminal records). Applicants' criminal records should include not only felony convictions but also any other moral turpitude offenses (involving dishonesty, false statement, and information on pending cases). The PSOEAA also allows businesses to do background checks of their employees every twelve months, enabling the employers to make sure that their employees remain qualified for their security jobs during their employment. It also must be mentioned that the state of California, for effective management of its private security sector, has established a professional government authority, the Bureau of Security and Investigative Services, a tacit recognition that the private security industry needs to be thoroughly, professionally, and actively managed by a professional government authority. The American system provides a workable model for the Korean private security industry. First, this paper argues that the Korean private security industry should implement a more strict criminal background check system similar to that required by the PSOEAA. Second, it recommends that an independent professional government authority be established to oversee and enforce regulation of Korea's private security industry. Finally, this article suggests that education and training course be implemented to provide both diverse training as well as specialization and phasing.