• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.374-386
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• 2022

Nowadays, research in deep learning leveraged automated computing and networking paradigm evidenced rapid contributions in terms of Software Defined Networking (SDN) and its diverse security applications while handling cybercrimes. SDN plays a vital role in sniffing information related to network usage in large-scale data centers that simultaneously support an improved algorithm design for automated detection of network intrusions. Despite its security protocols, SDN is considered contradictory towards DDoS attacks (Distributed Denial of Service). Several research studies developed machine learning-based network intrusion detection systems addressing detection and mitigation of DDoS attacks in SDN-based networks due to dynamic changes in various features and behavioral patterns. Addressing this problem, this research study focuses on effectively designing a multistage hybrid and intelligent deep learning classifier based on modified deep forest classification to detect DDoS attacks in SDN networks. Experimental results depict that the performance accuracy of the proposed classifier is improved when evaluated with standard parameters.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1049-1058
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• 2022

HAR(Human Action Recognition) such as anomaly and object detection has become a trend in research field(s) that focus on utilizing Artificial Intelligence (AI) methods to analyze patterns of human action in crime-ridden area(s), media services, and industrial facilities. Especially, in real-time system(s) using video streaming data, HAR has become a more important AI-based research field in application development and many different research fields using HAR have currently been developed and improved. In this paper, we propose and analyze a deep-learning-based HAR that provides more efficient scheme(s) using an intelligent AI models, such system can be applied to media services using RGB video streaming data usage without feature extraction pre-processing. For the method, we adopt Slowfast based on the Deep Neural Network(DNN) model under an open dataset(HMDB-51 or UCF101) for improvement in prediction accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1009-1011
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• 2011

With the growth of Smart Grid technologies, security and privacy will become the most important issues and more attention should be paid. There are some existing solutions about anonymization of smart meters, however, they still have some potential threats. In this paper, we describe an enhanced method to protect the privacy of consumer data. When metering data are required by a utility or the electrical energy distribution center for operational reasons, data are delivered not with the real IDs but with temporary IDs. In addition, these temporary IDs are changed randomly to prevent the attackers from analyzing the energy usage patterns. We also describe secure data transmission method for securing data delivered. In this way, we can enhance the privacy of Smart Grid System with low overhead.

• International Journal of Contents
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• v.18 no.1
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• pp.65-75
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• 2022

This study aims to reveal the various differences within the factors that influence use satisfaction, continuous use intention, and media substitution intention, specifically in regard to the use motives and use behaviors of Netflix viewers. This study's results demonstrate that the following various factors affected use satisfaction, continuous use intention, and media substitution intention in differential ways. Firstly, the diversity of content influenced use satisfaction to a greater degree than social relations; and the diversity of content, social relations, and active participation all positively influenced continuous use intention. In other words, the more positively users appreciated the diversity of content, and the more strongly they had social relations, and the more actively they participated within communities, the greater degree to which they increased their continuous use intention for Netflix. However, the diversity of content and the convenience of use also had a negative effect on the media substitution intention for Netflix, which means that the more diverse the content and the more convenient the use of Netflix, the fewer the number of users who intended to cancel Netflix and subscribe to another OTT service or resubscribe to traditional media sources.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.400-401
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• 2021

최근 전기자동차의 수요가 급증하면서 전기차 충전 인프라에 대한 요구도 높아지고 있다. 따라서 전기차의 보급률에 비해 부족한 충전 인프라의 한계를 극복하기 위해 가정에서 쉽게 사용할 수 있는 이동형 충전기의 사용률도 높아지고 있다. 고속 충전소와 다르게 이동형 충전기의 경우 사용자의 특성(배터리용량, 주행량, 충전빈도, 충전시간 등)에 맞춰 충전 시간과 장소, 요금 등을 제안할 수 있는 맞춤형 부가 서비스를 창출할 수 있다. 이를 통해 결과적으로 주택 또는 건물과 도시 수준에서의 전기차 충전을 위한 전력 부하를 절감하는 효과를 가져올 수 있다. 본 논문은 이러한 부가 서비스를 창출하기 위한 이동형 충전기 사용자 데이터의 특성들을 분석한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.794-795
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• 2023

금융 소외 계층은 여전히 보이스피싱과 같은 금융 범죄의 위험에 직면하고 있다. 본 논문에서는 이러한 위험을 완화하기 위해 모바일 애플리케이션을 통한 보이스피싱 예방을 강화하고자 한다. 이를 위해 AI 기술을 적용하여 데이터를 추출, 학습 및 분석하는 혁신적인 보안 기술을 개발하고자 한다. 이러한 기술의 적용을 통해 금융 소외 계층을 더 효과적으로 보호하고 금융범죄로부터 보안을 강화하는데 기여할 것으로 기대한다.

• Microbiology and Biotechnology Letters
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• v.51 no.4
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• pp.555-558
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• 2023

Staphylococcus aureus is a prominent multidrug-resistant pathogen known for its resistance to a variety of antibiotics. To combat this, a wide range of antibiotics, including quinolones, is utilized. While the efficacy of quinolones against S. aureus has been established, the rise in quinolone-resistant strains, particularly in methicillin-resistant S. aureus (MRSA), has necessitated a shift in their usage patterns. Genomic sequencing plays a crucial role as it offers insights into the genetic mechanisms of resistance. Thus, we report the complete genome sequence of an oxolinic acid-resistant strain of S. aureus isolated from sweet potato leaves, a crop commonly cultivated in Korea.

• Electronics and Telecommunications Trends
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• v.39 no.4
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• pp.32-41
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• 2024

Building energy management plays a crucial role in improving energy efficiency and optimizing energy usage. To achieve this, it is important to monitor and analyze energy-related data from buildings in real time using sensors to understand energy consumption patterns and establish optimal operational strategies. Because of the uncertainties in building energy-related data, there are challenges in analyzing these data and formulating operational strategies based on them. Artificial intelligence (AI) technology can help overcome these challenges. This paper investigates past and current research trends in AI technology and examines its future prospects for building energy management. By performing prediction and analysis based on energy consumption or supply data, the future energy demands of buildings can be forecasted and energy consumption can be optimized. Additionally, data related to the surrounding environment, occupancy, and other building energy-related factors can be collected and analyzed using sensors to establish operational strategies aimed at further reducing energy consumption and increasing efficiency. These technologies will contribute to cost savings and help minimize environmental impacts for building owners and operators, ultimately facilitating sustainable building operations.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.125-132
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• 2014

Since frequent pattern mining was proposed in order to search for hidden, useful pattern information from large-scale databases, various types of mining approaches and applications have been researched. Especially, frequent graph pattern mining was suggested to effectively deal with recent data that have been complicated continually, and a variety of efficient graph mining algorithms have been studied. Graph patterns obtained from graph databases have their own importance and characteristics different from one another according to the elements composing them and their lengths. However, traditional frequent graph pattern mining approaches have the limitations that do not consider such problems. That is, the existing methods consider only one minimum support threshold regardless of the lengths of graph patterns extracted from their mining operations and do not use any of the patterns' weight factors; therefore, a large number of actually useless graph patterns may be generated. Small graph patterns with a few vertices and edges tend to be interesting when their weighted supports are relatively high, while large ones with many elements can be useful even if their weighted supports are relatively low. For this reason, we propose a weight-based frequent graph pattern mining algorithm considering length-decreasing support constraints. Comprehensive experimental results provided in this paper show that the proposed method guarantees more outstanding performance compared to a state-of-the-art graph mining algorithm in terms of pattern generation, runtime, and memory usage.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.69-85
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• 2006

Broadcast has been often used to disseminate the frequently requested data efficiently to a large volume of mobile clients over a single or multiple channels. Conventional concurrency control protocols for mobile transactions are not suitable for the wireless broadcast environments due to the limited bandwidth of the up-link communication channel. In wireless broadcast environments, the server often broadcast different data items with different frequency to incorporate the data access patterns of mobile transactions. The previously proposed concurrency control protocols for mobile transactions in wireless broadcast environments are focused on the mobile transactions with uniform data access patterns. However, these protocols perform poorly when the data access pattern of update mobile transaction are not uniform but skewed. The update mobile transactions with skewed data access patterns will be frequently aborted and restarted due 4o the update conflict of the same data items with a high access frequency. In this paper, we propose an energy-efficient concurrence control protocol for mobile transactions with skewed data access as well as uniform data access patterns. Our protocol use a random back-off technique to avoid the frequent abort and restart of update mobile transactions. We present in-depth experimental analysis of our method by comparing it with existing concurrency control protocols. Our performance analysis show that it significantly decrease the average response time, the amount of upstream and downstream bandwidth usage over existing protocols.