• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.707-714
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• 2023

The Internet of Things, which is the key factor of the 4th industrial revolution, are apt to apply to many systems. The existing security mechanism cannot be realized with limited resources such as low capacity of devices and sensors. In order to apply IoT system, a new structure and ultra-lightweight encryption is required. In this paper, we analyzed security issues that can operate in Internet-based smart home networks, and to solve the critical issues against these attacks, technologies for device protection between heterogeneous devices. Security requirements are required to protect from attacks. Therefore, we analyzed the demands and requirements for its application by analyzing the security architecture and features in smart home network.

• PNF and Movement
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• v.22 no.1
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• pp.43-54
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• 2024

Purpose: Falls among persons older than 65 years are a significant concern due to their frequency and severity. This study aimed to develop a vest-type embedded artificial intelligence (AI) system capable of detecting and predicting falls in various scenarios. Methods: In this study, we established and developed a vest-type embedded AI system to judge and predict falls in various directions and situations. To train the AI, we collected data using acceleration and gyroscope values from a six-axis sensor attached to the seventh cervical and the second sacral vertebrae of the user, considering accurate motion analysis of the human body. The model was constructed using a neural network-based AI prediction algorithm to anticipate the direction of falls using the collected pedestrian data. Results: We focused on developing a lightweight and efficient fall prediction model for integration into an embedded AI algorithm system, ensuring real-time network optimization. Our results showed that the accuracy of fall occurrence and direction prediction using the trained fall prediction model was 89.0% and 78.8%, respectively. Furthermore, the fall occurrence and direction prediction accuracy of the model quantized for embedded porting was 87.0 % and 75.5 %, respectively. Conclusion: The developed fall detection and prediction system, designed as a vest-type with an embedded AI algorithm, offers the potential to provide real-time feedback to pedestrians in clinical settings and proactively prepare for accidents.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.55-59
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• 2010

Currently in a Ubiquitous Sensor Network (USN) research field, supporting mobility is recognized as an important technology. MIPv6 and Network Mobility(NEMO) Basic Support Protocol are standard protocols to support mobility in the Internet. However, if they are applied to USN with no modification, handoff performance decreases due to the size of their binding message. An existing lightweight protocol for NEMO protocol has a compatibility problem of Sequence Num. and does not optimally compress binding messages considering 6LoWPAN network structure and addressing. This paper proposes optimal header compression which supports node-based mobility and network-based mobility. Our optimal compression technique compresses a 32bytes binding update(BU) message and a 12bytes binding ACK(BA) message of MIPv6 into 13bytes and 3bytes, and a 40bytes BU message and a 12bytes BA message of NEMO protocol into 13bytes and 3bytes. The result shows that our protocol compresses 15bytes (NEMO-BU) and 1byte (NEMO-BA) more than the existing protocol and achieves 8.72% handoff performance improvement.

• The KIPS Transactions:PartC
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• v.15C no.5
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• pp.351-358
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• 2008

Recently, as network flooding attacks such as DoS/DDoS and Internet Worm have posed devastating threats to network services, rapid detection and proper response mechanisms are the major concern for secure and reliable network services. However, most of the current Intrusion Detection Systems(IDSs) focus on detail analysis of packet data, which results in late detection and a high system burden to cope with high-speed network environment. In this paper we propose a lightweight and fast detection mechanism for traffic flooding attacks. Firstly, we use SNMP MIB statistical data gathered from SNMP agents, instead of raw packet data from network links. Secondly, we use a machine learning approach based on a Support Vector Machine(SVM) for attack classification. Using MIB and SVM, we achieved fast detection with high accuracy, the minimization of the system burden, and extendibility for system deployment. The proposed mechanism is constructed in a hierarchical structure, which first distinguishes attack traffic from normal traffic and then determines the type of attacks in detail. Using MIB data sets collected from real experiments involving a DDoS attack, we validate the possibility of our approaches. It is shown that network attacks are detected with high efficiency, and classified with low false alarms.

• Smart Media Journal
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• v.10 no.1
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• pp.88-98
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• 2021

As blockchain technology has emerged as a security issue for IoT, technology which integrates block chain into IoT is being studied. In this paper is a research concerning token-based IoT service access control technology for data sharing, which propose a possessor focused data sharing technic by using the permissioned blockchain. To share IoT health data, a Hyperledger Fabric Network consisting of three organizations was designed to provide a way to share data by applying different access control policies centered on device owners for different services. In the proposed system, the device owner issues access control tokens with different security levels applied to the participants in the organization, and the token issue information is shared through the distributed ledger of the HFN. In IoT, it is possible to lightweight the access control processing of IoT devices by granting tokens to service requesters who request access to data. Furthmore, by sharing token issuance information among network participants using HFN, the integrity of the token is guaranteed and all network participants can trust the token. The device owners can trust that their data is being used within their authorized rights, and control the collection and use of data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.1-7
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• 2016

As new information and communication technologies evolve, security threats are also becoming increasingly intelligent and advanced. In this paper, we analyze the time series data continuously entered through a series of periods from the network device or lightweight IoT (Internet of Things) devices by using the statistical technique and propose a system to detect abnormal behaviors of the device or abnormality based on the analysis results. The proposed system performs the first level abnormal detection by using previously entered data set, thereafter performs the second level anomaly detection according to the trust bound configured by using stored time series data based on time attribute or group attribute. Multi-level analysis is able to improve reliability and to reduce false positives as well through a variety of decision data set.

• Proceedings of the Materials Research Society of Korea Conference
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• 2009.11a
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• pp.14-14
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• 2009

High frequency electromagnetic(EM) waves are increasingly being applied in industries because of saturationat lower frequency bands as a result of huge demand. However, electromagneticinterference (EMI) has become a serious problem, and as a result, highfrequency EM absorbers are now being extensively studied. Also, recentdevelopments in absorber technology have focused on producing absorbers thatare thin, flexible, and strong. Hence, one-dimension ferrous nano-materials area potential research field, because of their interesting electronic andmagnetic properties. Commercially, EM wave absorbing products are made ofcomposites, which blend the insulating polymer with magnetic fillers. Inparticular, the shape of the magnetic fillers, such flaky, acicular, or fibrousmagnetic metal particles, rather than spherical, is essential for synthesizingthin and lightweight EM wave absorbers with higher permeability. High aspectratio materials exhibit a higher permeability value and therefore betterabsorption of the EM wave, because of electromagnetic anisotropy. Nanowires areusually fabricated by drawing, template synthesis, phase separation, selfassembly, and electrospinning with a thermal treatment and reduction process.Producing nanowires by the electrospinning method involves a conventionalsol-gel process that is simple, unique, and cost-effective. In thispresentation, Magnetic nanowire and dielectric materials coated magneticnanowire with a high aspect ratio were successfully synthesized by theelectrospinning process with heat treatment and reduction. In addition toestimating the EM wave absorption ability of the synthesized magnetic anddielectric materials coated magnetic nanowire with a network analyzer, weinvestigated the possibility of using these nanowires as high-frequency EM waveabsorbers. Furthermore, a wide variety of topics will be discussed such as thetransparent conducting nanowire and semiconducting nanowire/tube with theelectrospinning process.

• Korean Journal of Materials Research
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• v.30 no.4
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• pp.155-159
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• 2020

Nanoporous silica aerogel insulation material is both lightweight and efficient; it has important value in the fields of aerospace, petrochemicals, electric metallurgy, shipbuilding, precision instruments, and so on. A theoretical calculation model and experimental measurement of equivalent thermal conductivity for nanoporous silica aerogel insulation material are introduced in this paper. The heat transfer characteristics and thermal insulation principle of aerogel nano are analyzed. The methods of SiO₂ aerogel production are compared. The pressure range of SiO₂ aerogel is 1Pa-atmospheric pressure; the temperature range is room temperature-900K. The pore diameter range of particle SiO₂ aerogel is about 5 to 100 nm, and the average pore diameter range of about 20 ~ 40 nm. These results show that experimental measurements are in good agreement with theoretical calculation values. For nanoporous silica aerogel insulation material, the heat transfer calculation method suitable for nanotechnology can precisely calculate the equivalent thermal conductivity of aerogel nano insulation materials. The network structure is the reason why the thermal conductivity of the aerogel is very low. Heat transfer of materials is mainly realized by convection, radiation, and heat transfer. Therefore, the thermal conductivity of the heat transfer path in aerogel can be reduced by nanotechnology.

• ETRI Journal
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• v.39 no.3
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• pp.406-416
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• 2017

Malware propagated via the World Wide Web is one of the most dangerous tools in the realm of cyber-attacks. Its methodologies are effective, relatively easy to use, and are developing constantly in an unexpected manner. As a result, rapidly detecting malware propagation websites from a myriad of webpages is a difficult task. In this paper, we present LoGos, an automated high-interaction dynamic analyzer optimized for a browser-based Windows virtual machine environment. LoGos utilizes Internet Explorer injection and API hooks, and scrutinizes malicious behaviors such as new network connections, unused open ports, registry modifications, and file creation. Based on the obtained results, LoGos can determine the maliciousness level. This model forms a very lightweight system. Thus, it is approximately 10 to 18 times faster than systems proposed in previous work. In addition, it provides high detection rates that are equal to those of state-of-the-art tools. LoGos is a closed tool that can detect an extensive array of malicious webpages. We prove the efficiency and effectiveness of the tool by analyzing almost 0.36 M domains and 3.2 M webpages on a daily basis.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.2
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• pp.119-127
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• 2004

Butterfly valves are popularly used in service in the industrial and water works pipeline systems with large diameter because of its lightweight, simple structure and the rapidity of its manipulation. Sometimes cavitation can occur. resulting in noise, vibration and rapid deterioration of the valve trim, and do not allow further operation. Thus, the monitoring of cavitation is of economic interest and is very importance in industry. This paper proposes a condition monitoring scheme using statistical feature evaluation and support vector machine (SVM) to detect the cavitation conditions of butterfly valve which used as a flow control valve at the pumping stations. The stationary features of vibration signals are extracted from statistical moments. The SVMs are trained, and then classify normal and cavitation conditions of control valves. The SVMs with the reorganized feature vectors can distinguish the class of the untrained and untested data. The classification validity of this method is examined by various signals that are acquired from butterfly valves in the pumping stations and compared the classification success rate with those of self-organizing feature map neural network.