• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.197-202
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• 2022

IoT (Internet of Things) devices are being attacked by malware due to many security vulnerabilities, such as the use of weak IDs/passwords and unauthenticated firmware updates. However, due to the diversity of CPU architectures, it is difficult to set up a malware analysis environment and design features. In this paper, we design time series features using the byte sequence of executable files to represent independent features of CPU architectures, and analyze them using recurrent neural networks. The proposed feature is a fixed-length time series pattern extracted from the byte sequence by calculating partial entropy and applying linear interpolation. Temporary changes in the extracted feature are analyzed by RNN and LSTM. In the experiment, the IoT malware detection showed high performance, while low performance was analyzed in the malware family classification. When the entropy patterns for each malware family were compared visually, the Tsunami and Gafgyt families showed similar patterns, resulting in low performance. LSTM is more suitable than RNN for learning temporal changes in the proposed malware features.

• KNOM Review
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• v.23 no.1
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• pp.10-17
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• 2020

As the threat of Distributed Denial-of-Service attacks that exploit weakly secure IoT devices has spread, research on source-side Denial-of-Service attack detection is being activated to quickly detect the attack and the location of attacker. In addition, a collaborative source-side attack detection technique that shares detection results of source-side networks located at individual sites is also being activated to overcome regional limitations of source-side detection. In this paper, we evaluate the performance of a collaborative source-side DDoS attack detection using statistical weights. The statistical weight is calculated based on the detection rate and false positive rate corresponding to the time zone of the individual source-side network. By calculating weighted sum of the source-side DoS attack detection results from various sites, the proposed method determines whether a DDoS attack happens. As a result of the experiment based on actual DNS request to traffic, it was confirmed that the proposed technique reduces false positive rate 2% while maintaining a high attack detection rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.1060-1070
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• 2022

With the development of information technology, the size of the system has become larger and diversified, and the existing role-based access control has faced limitations. Blockchain technology is being used in various fields by presenting new solutions to existing security vulnerabilities. This paper suggests efficient role-based access control in a blockchain where the required gas and processing time vary depending on the access frequency and capacity of the storage. The proposed method redefines the role of reusable units, introduces a hierarchical structure that can efficiently reflect dynamic states to enhance efficiency and scalability, and includes user-centered authentication functions to enable cryptocurrency linkage. The proposed model was theoretically verified using Markov chain, implemented in Ethereum private network, and compared experiments on representative functions were conducted to verify the time and gas efficiency required for user addition and transaction registration. Based on this in the future, structural expansion and experiments are required in consideration of exception situations.

• Smart Media Journal
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• v.12 no.7
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• pp.59-67
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• 2023

Recently, research on behavior analysis tracking human posture and movement has been actively conducted. In particular, OpenPose, an open-source software developed by CMU in 2017, is a representative method for estimating human appearance and behavior. OpenPose can detect and estimate various body parts of a person, such as height, face, and hands in real-time, making it applicable to various fields such as smart healthcare, exercise training, security systems, and medical fields. In this paper, we propose a method for classifying four exercise movements - Squat, Walk, Wave, and Fall-down - which are most commonly performed by users in the gym, using OpenPose-based deep learning models, DNN and CNN. The training data is collected by capturing the user's movements through recorded videos and real-time camera captures. The collected dataset undergoes preprocessing using OpenPose. The preprocessed dataset is then used to train the proposed DNN and CNN models for exercise movement classification. The performance errors of the proposed models are evaluated using MSE, RMSE, and MAE. The performance evaluation results showed that the proposed DNN model outperformed the proposed CNN model.

• KNOM Review
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• v.22 no.3
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• pp.1-12
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• 2019

Recently, researching using big data and AI has emerged as a major issue in the ICT field. But, the size of big data for research is growing exponentially. In addition, users of data transmission of existing network method suggest that the problem the time taken to send and receive big data is slower than the time to copy and send the hard disk. Accordingly, researchers require dynamic and flexible network technology that can transmit data at high speed and accommodate various network structures. SDN/NFV technologies can be programming a network to provide a network suitable for the needs of users. It can easily solve the network's flexibility and security problems. Also, the problem with performing AI is that centralized data processing cannot guarantee real-time, and network delay occur when traffic increases. In order to solve this problem, the edge-computing technology, should be used which has moved away from the centralized method. In this paper, we investigate the concept and research trend of SDN, NFV, and edge-computing technologies, and analyze the trends of data central network technologies used by combining these three technologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.916-937
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• 2023

Most of the existing Distributed Denial-of-Service mitigation schemes in Software-Defined Networking are only implemented in the network domain managed by a single controller. In fact, the zombies for attackers to launch large-scale DDoS attacks are actually not in the same network domain. Therefore, abnormal traffic of DDoS attack will affect multiple paths and network domains. A single defense method is difficult to deal with large-scale DDoS attacks. The cooperative defense of multiple domains becomes an important means to effectively solve cross-domain DDoS attacks. We propose an efficient multi-domain DDoS cooperative defense mechanism by integrating blockchain and SDN architecture. It includes attack traceability, inter-domain information sharing and attack mitigation. In order to reduce the length of the marking path and shorten the traceability time, we propose an AS-level packet traceability method called ASPM. We propose an information sharing method across multiple domains based on blockchain and smart contract. It effectively solves the impact of DDoS illegal traffic on multiple domains. According to the traceability results, we designed a DDoS attack mitigation method by replacing the ACL list with the IP address black/gray list. The experimental results show that our ASPM traceability method requires less data packets, high traceability precision and low overhead. And blockchain-based inter-domain sharing scheme has low cost, high scalability and high security. Attack mitigation measures can prevent illegal data flow in a timely and efficient manner.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.139-148
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• 2023

Current food-traceability platforms suffer from problems such as inconsistent traceability standards, a lack of public credibility, and slow access to data. In this work, a combined code and identification method was designed that can achieve more secure product traceability using the dual anti-counterfeiting technology of a QR code and a hidden code. When the QR code is blurry, the hidden code can still be used to effectively identify food information. Based on this combined code, a food-safety traceability platform was developed. The platform follows unified encoding standards and provides standardized interfaces. Based on this innovation, the platform not only can serve individual food-traceability systems development, but also connect existing traceability systems. These will help to solve the problems such as non-standard traceability content, inconsistent processes, and incompatible system software. The experimental results show that the combined code has higher accuracy. The food-safety traceability platform based on the combined code improves the safety of the traceability process and the integrity of the traceability information. The innovation of this paper is invoking the combined code united the QR code's rapidity and the hidden code's reliability, developing a platform that uses a unified coding standard and provides a standardized interface to resolve the differences between multi-food-traceability systems. Among similar systems, it is the only one that has been connected to the national QR code identification platform. The project has made profits and has significant economic and social benefits.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.29-39
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• 2022

The outbreak of the COVID-19 pandemic has had a significant impact on the Vietnamese economy. In the midst of a complex disease that compelled people to limit their interaction, customers' shopping habits shifted from "offline" to "online" transactions. Mobile payments have also grown in popularity. The goal of this study is to figure out what factors influence the use of mobile payments by hotel clients in Can Tho after COVID-19. The research team also examines how those factors influence customers' willingness to use mobile payment and makes recommendations to better the current situation. Primary data was collected from 227 persons using online surveys and processed with SPSS software for this study. To analyze the correlation relationship between the elements determining the intention to use, the Cronbach alpha, EFA, Correlation, and Regression methods used to assess the scale are applied. Perceived Trustworthiness, Perceived Usefulness, and Perceived Ease of Use all have positive effects on customers' propensity to use, according to the findings. Perceived Security, on the other hand, has no bearing. The findings of this study have significant theoretical and practical implications for the development of mobile payment services in Can Tho, particularly following the implementation of COVID-19.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.168-168
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• 2020

This study is to evaluate the drought-related bigdata characteristics published from South Korean by developing crawler. The 5 years (2013 ~ 2017) drought-related posted articles were collected from Korean internet search engine 'NAVER' which contains 13 main and 81 local daily newspapers. During the 5 years period, total 40,219 news articles including 'drought' word were found using crawler. To filter the homonyms liken drought to soccer goal drought in sports, money drought economics, and policy drought in politics often used in South Korea, the quality control was processed and 47.8 % articles were filtered. After, the 20,999 (52.2 %) drought news articles of this study were classified into four categories of water deficit (WD), water security and support (WSS), economic damage and impact (EDI), and environmental and sanitation impact (ESI) with 27, 15, 13, and 18 drought-related keywords in each category. The WD, WSS, EDI, and ESI occupied 41.4 %, 34.5 %, 14.8 %, and 9.3 % respectively. The drought articles were mostly posted in June 2015 and June 2017 with 22.7 % (15,097) and 15.9 % (10,619) respectively. The drought news articles were spatiotemporally compared with SPI (Standardized Precipitation Index) and RDI (Reservoir Drought Index) were calculated. They were classified into administration boundaries of 8 main cities and 9 provinces in South Korea because the drought response works based on local government unit. The space-time clustering between news articles (WD, WSS, EDI, and ESI) and indices (SPI and RDI) were tried how much they have correlation each other. The spatiotemporal clusters detection was applied using SaTScan software (Kulldorff, 2015). The retrospective and prospective cluster analyses were conducted for past and present time to understand how much they are intensive in clusters. The news articles of WD, WSS and EDI had strong clusters in provinces, and ESI in cities.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.179-187
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• 2023

Consumer trust plays a crucial role in the development of the sharing economy. This study primarily focuses on the factors influencing consumer trust and examines the case of ofo, a former leader in China's bike-sharing industry. This paper analyzes the decline in consumer trust in ofo, which can be attributed to internal management issues and the near-bankruptcy situation. The "difficulty in refunds" issue faced by ofo since December 2018 has been growing continuously, and this study explores the factors influencing trust and distrust in this context. By considering product factors (quality), platform factors (payment security, privacy protection, reputation), and social factors (social norms, government regulation) as independent variables, the study analyzes the factors affecting consumer trust. The analysis results revealed that as consumers' distrust towards shared bikes increases, their switching intention also increases. The company's reputation and social norms were found to influence both trust and distrust, while government regulation was found to influence trust. The research findings provide insights relevant to sharing economy platforms and offer guidance for future studies.