• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.275-280
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• 2021

With the development of communication networks, the processes of exchanging and transmitting information rapidly developed. As millions of images are sent via social media every day, also wireless sensor networks are now used in all applications to capture images such as those used in traffic lights, roads and malls. Therefore, there is a need to reduce the size of these images while maintaining an acceptable degree of quality. In this paper, we use Python software to apply K-mean Clustering algorithm to compress RGB images. The PSNR, MSE, and SSIM are utilized to measure the image quality after image compression. The results of compression reduced the image size to nearly half the size of the original images using k = 64. In the SSIM measure, the higher the K, the greater the similarity between the two images which is a good indicator to a significant reduction in image size. Our proposed compression technique powered by the K-Mean clustering algorithm is useful for compressing images and reducing the size of images.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.125-131
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• 2022

The diversity and complexity of criminal, administrative and civil cases resolved by the courts makes it difficult to develop universal automated tools for the analysis and evaluation of justice. However, big data generated in the scope of justice gives hope that this problem will be resolved as soon as possible. The big data applying makes it possible to identify typical options for resolving cases, form detailed rules for the individualization of a court decision, and correlate these rules with an abstract provisions of law. This approach allows us to somewhat overcome the contradiction between the abstract and the concrete in law, to automate the analysis of justice and to model e-justice for scientific and practical purposes. The article presents the results of using dimension reduction, SHAP value, and p-value to identify, analyze and evaluate the individualization of justice and the differentiation of legal regulation. Processing and analysis of arrays of court decisions by computational methods make it possible to identify the typical views of courts on questions of fact and questions of law. This knowledge, obtained automatically, is promising for the scientific study of justice issues, the improvement of the prescriptions of the law and the probabilistic prediction of a court decision with a known set of facts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1674-1688
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• 2023

The remarkable advancement of quantum steganography offers enhanced security for quantum communications. However, there is a significant concern regarding the potential misuse of this technology. Moreover, the current research on identifying malicious quantum steganography is insufficient. To address this gap in steganalysis research, this paper proposes a specialized quantum steganalysis algorithm. This algorithm utilizes quantum machine learning techniques to detect steganography in general quantum secure communication schemes that are based on pure states. The algorithm presented in this paper consists of two main steps: data preprocessing and automatic discrimination. The data preprocessing step involves extracting and amplifying abnormal signals, followed by the automatic detection of suspicious quantum carriers through training on steganographic and non-steganographic data. The numerical results demonstrate that a larger disparity between the probability distributions of steganographic and non-steganographic data leads to a higher steganographic detection indicator, making the presence of steganography easier to detect. By selecting an appropriate threshold value, the steganography detection rate can exceed 90%.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.85-92
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• 2024

In recent years, Bitcoin and Ethereum have witnessed a surge in trading activity, driven by venture capital investment and funding through initial coin offerings (ICOs) and initial exchange offerings (IEOs). This heightened interest has led to kickstarting a vibrant ecosystem for blockchain development. The total number of cryptocurrencies listed on CoinMarketCap.com has reached 2,274 highlights how dynamic and wide blockchain development landscape has grown. In blockchain development, new blockchain projects are being created by forking blockchains inspired by major cryptocurrencies such as Bitcoin and Ethereum. These projects aim to address the perceived shortcomings and improve existing technologies. Altcoins, representing these alternative cryptocurrencies, are an ongoing industry effort to improve performance and security with enhancement proposals such as Bitcoin Improvement Proposals (BIP), Ethereum Improvement Proposals (EIP), and EOSIO Enhancement Proposals (EEP). With competitive attempts to improve blockchain performance and security, an ongoing performance race between various blockchains has taken shape, each claiming its own performance advantages. In this paper, we describe the transactions contained in the blocks of each representative blockchain, and find the factors that affect the transactions per second (TPS) through transaction processing and block generation processes, and suggest their relationship with scalability.

• Journal of Energy Engineering
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• v.23 no.2
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• pp.93-101
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• 2014

How to determine the extent of national energy security? In this paper, we estimate it by comparative analysis of South Korea and other OECD countries in terms of energy diversity (fuel diversity). Energy security consists of 4 key factors such as availability, accessibility, acceptability, affordability. Especially the importance of accessibility can grow as local imbalance of supply and demand increases. As a proxy of the accessibility, fuel diversity can be a significant indicator to estimate a measure of energy security. In this paper, we use Shannon-Wiener index to measure energy diversity. If fuel diversity increases, the stability of energy security also should increase, because of the smoothing effect to lessen dependence on key energy sources. In 2012 Korean growth rate of H-index (energy diversity) is 18.38%, which is higher than other OECD countries. However, Korean H-index itself is 1.93, lesser than other countries. Shift from oil to coals/gas within fossil fuels has more impact on H-index than weight transition from fossil fuels to renewable energies in Korea. We conclude that more renewable energy is an effective solution to achieve higher energy diversity and ultimately higher energy security as the same as the German case.

• The Journal of Society for e-Business Studies
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• v.16 no.2
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• pp.129-142
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• 2011

Traditionally research in Service Oriented Architecture(SOA) security has focused primarily on exploiting standards and solutions separately. There exists no unified methodology for SOA security to manage risks at the enterprise level. It needs to analyze preliminarily security threats and to manage enterprise risks by identifying vulnerabilities of SOA. In this paper, we propose a metric-based vulnerability assessment method using dynamic properties of services in SOA. The method is to assess vulnerability at the architecture level as well as the service level by measuring run-time dependency between services. The run-time dependency between services is an important characteristic to understand which services are affected by a vulnerable service. All services which directly or indirectly depend on the vulnerable service are exposed to the risk. Thus run-time dependency is a good indicator of vulnerability of SOA.

• Convergence Security Journal
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• v.20 no.3
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• pp.21-28
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• 2020

In recent years, hacking and malware techniques have evolved and become sophisticated and complex, and numerous cyber-attacks are constantly occurring in various fields. Among them, the most widely used route for compromise incidents such as information leakage and system destruction was found to be E-Mails. In particular, it is still difficult to detect and identify E-Mail APT attacks that employ zero-day vulnerabilities and social engineering hacking techniques by detecting signatures and conducting dynamic analysis only. Thus, there has been an increased demand for indicators of compromise (IOC) to identify the causes of malicious activities and quickly respond to similar compromise incidents by sharing the information. In this study, we propose a method of extracting various forensic artifacts required for detecting and investigating Hacking E-Mails, which account for large portion of damages in security incidents. To achieve this, we employed a digital forensic indicator method that was previously utilized to collect information of client-side incidents.

• Journal of Digital Convergence
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• v.13 no.2
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• pp.115-120
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• 2015

As the cloud services, the underlying technology of the digital convergence environment, have been widely adopted in the business, personal information protection has been recognized as one of the major issues to resolve. When cloud services are used to process the personal information, the personal information protection law speculates the establishment of a contract or service level agreement(SLA). This research presents 7 privacy indicators and 13 metrics which can be included in cloud SLA, based on the analysis of related regulation and standards and the SMART(Specific, Measurable, Action-oriented, Relevant and Timely) model. The proposed indicators are examined using the Focus Group Interview method in terms of materiality and feasibility. The results show that all the proposed indicators are meaningful and useful.

• Journal of the Korean Society of Systems Engineering
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• v.15 no.1
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• pp.34-42
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• 2019

Receiving great attention of IoT and 4th industrial revolution, the necessity comes to the fore of the plant system which aims making it smart and effective. Smart Factory is the key realm of IoT to apply with the concept to optimize the entire process and it presents a new and flexible production paradigm based on the collected data from numerous sensors installed in a plant. Especially, the wireless sensor network technology is receiving attention as the key technology of Smart Factory, researches to interface those technology is actively in progress. In addition, IoT devices for plant industry security and high reliable network protocols are under development to cope with high-risk plant facilities. In the meanwhile, Blockchain can support high security and reliability because of the hash and hash algorithm in its core structure and transaction as well as the shared ledger among all nodes and immutability of data. With the reason, this research presents Blockchain as a method to preserve security and reliability of the wireless communication technology. In regard to that, it establishes some of key concepts of the possibility on the blockchain based IoT Edge devices for Plant O&M (Operations and Maintenance), and fulfills performance verification with test devices to present key indicator data such as transaction elapsed time and CPU consumption rate.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1105-1114
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• 2021

Due to the recent surge in popularity of cryptocurrency, the threat of cryptojacking, a malicious code for mining cryptocurrencies, is increasing. In particular, web-based cryptojacking is easy to attack because the victim can mine cryptocurrencies using the victim's PC resources just by accessing the website and simply adding mining scripts. The cryptojacking attack causes poor performance and malfunction. It can also cause hardware failure due to overheating and aging caused by mining. Cryptojacking is difficult for victims to recognize the damage, so research is needed to efficiently detect and block cryptojacking. In this work, we take representative distinct symptoms of cryptojacking as an indicator and propose a new architecture. We utilized the K-Nearst Neighbors(KNN) model, which trained computer performance indicators as behavior-based dynamic analysis techniques. In addition, a K-means model, which trained the frequency of malicious script words for script similarity-based static analysis techniques, was utilized. The KNN model had 99.6% accuracy, and the K-means model had a silhouette coefficient of 0.61 for normal clusters.