• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.363-370
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• 2021

The perimeter intrusion detection system is very important in physical security. In this study, a micro smart device (module) using MEMS sensor was developed in IoT environment for external intrusion detection. The outer intrusion detection system applying the smart device developed in this study is installed in various installation environments, such as barbed wire of various materials and shapes, the city center, the beach, and the mountain, so that it can detect external intrusion and its location as well as false alarms. As a smart sensor that can minimize the false alarm rate and economical construction cost, it is expected that it can be used for the safe operation of major facilities and prevention of disasters and crimes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1035-1043
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• 2022

With the spread of the Internet of Things (IoT), cloud computing, and big data, the need for high-speed encryption for applications is emerging. GPU optimization can be used to validate cryptographic analysis results or reduced versions theoretically obtained by the GPU in a reasonable time. In this paper, PIPO lightweight encryption implemented in various environments was implemented on GPU. Optimally implemented considering the brute force attack on PIPO. In particular, the optimization implementation applying the bit slicing technique and the GPU elements were used as much as possible. As a result, the implementation of the proposed method showed a throughput of about 19.5 billion per second in the RTX 3060 environment, achieving a throughput of about 122 times higher than that of the previous study.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.437-440
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• 2022

In modern society, the use of the image processing field is increasing rapidly due to the 4th industrial revolution and the development of IoT technology. In particular, edge detection is widely used in various fields as an essential preprocessing process in image processing applications such as image classification and object detection. Conventional methods for detecting an edge include a Sobel edge detection filter, a Roberts edge detection filter, a Prewitt edge detection filter, Laplacian of Gaussian (LoG), and the like. However, existing methods have the disadvantage of showing somewhat insufficient performance of edge detection characteristics in a low-light level environment with low contrast. Therefore, this paper proposes an edge detection algorithm based on contrast analysis to increase edge detection characteristics even in low-light level environments.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.319-325
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• 2023

In this study, we proposed a method for hyperparameter optimization in the building and training of a deep learning model designed to process point cloud data collected by a millimeter-wave radar system. The primary aim of this study is to facilitate the deployment of a baseline model in resource-constrained IoT devices. We evaluated a RadHAR baseline deep learning model trained on a public dataset composed of point clouds representing five distinct human activities. Additionally, we introduced a coarse-to-fine hyperparameter optimization procedure, showing substantial potential to enhance model efficiency without compromising predictive performance. Experimental results show the feasibility of significantly reducing model size without adversely impacting performance. Specifically, the optimized model demonstrated a 3.3% improvement in classification accuracy despite a 16.8% reduction in number of parameters compared th the baseline model. In conclusion, this research offers valuable insights for the development of deep learning models for resource-constrained IoT devices, underscoring the potential of hyperparameter optimization and model size reduction strategies. This work contributes to enhancing the practicality and usability of deep learning models in real-world environments, where high levels of accuracy and efficiency in data processing and classification tasks are required.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3003-3029
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• 2023

The rapid evolution of the IoT has paved the way for new opportunities in smart city domains, including e-health, smart homes, and precision agriculture. However, this proliferation of services demands effective SLAs between customers and service providers, especially for critical services. Difficulties arise in maintaining the integrity of such agreements, especially in vulnerable wireless environments. This study proposes a novel SLA management model that uses an SDN-Enabled WSN consisting of wireless nodes to interact with smart contracts in a straightforward manner. The proposed model ensures the persistence of network metrics and SLA provisions through smart contracts, eliminating the need for intermediaries to audit payment and compensation procedures. The reliability and verifiability of the data prevents doubts from the contracting parties. To meet the high-performance requirements of the blockchain in the proposed model, low-cost algorithms have been developed for implementing blockchain technology in wireless sensor networks with low-energy and low-capacity nodes. Furthermore, a cryptographic signature control code is generated by wireless nodes using the in-memory private key and the dynamic random key from the smart contract at runtime to prevent tampering with data transmitted over the network. This control code enables the verification of end-to-end data signatures. The efficient generation of dynamic keys at runtime is ensured by the flexible and high-performance infrastructure of the SDN architecture.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.7
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• pp.767-772
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• 2014

As temporary network, ad-hoc network has been effected by structures and implemented environments of networks. In this paper, transmission performance of lattice structure ad-hoc network, which is expected to use in sensor network and IoT(Internet of Things), is analyzed in point of intrusions and countermeasure for intrusion is suggested. In this paper, computer simulation based on NS-2 is used for performance analysis, VoIP(Voice over Internet Protocol) as a widely used service is chosen for performance measure. MOS(Mean Opinion Score) and call connection rate is used as performance parameter. As results of performance analysis, it is shown that for MOS, random network is better then lattice network at intrusion environments, but for call connection rate, lattice network is better then random network.

• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.117-128
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• 2019

As companies use increasing amounts of data more and more, people are more concerned about protecting their privacy. Many researches studies have been conducted with a to securely view of manage managing and share sharing private information securely using the Bblockchain technology. These studies have suggested a Bblockchain-based approaches to provide efficiency, scalability, data ownership, and systematic data lifecycles that were are the limitations of lacking in traditional access controls. More Sspecifically, these studies have introduced a new access control models, distributed hash tables, trusted execution environments, and hierarchical ID-based cryptographic mechanisms to provide reliable access control even in complex environments such as IoT Internet of Things. In this paperstudy, we present the criteria to for classifying the functional characteristics of the Bblockchain-based access control methods and derive the differentiateion between of each the several methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.661-667
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• 2018

Recently, the smart home market is growing due to the development of wireless communication technology and sensor devices, and various devices are being utilized. Such an IoT environment collects various vast amount of device information for intelligent services, receives services based on user information, controls various devices, and provides communication between different types of devices. However, with this growth, various security threats are occurring in the smart home environment. In fact, Proofpoint and HP warned about the cases of damage in a smart home environment and the severity of security vulnerabilities, and cases of infringement in various environments were announced. Therefore, in this paper, we have studied secure mutual authentication method between smart nodes used in smart home to solve security problems that may occur in smart home environment. In the case of the proposed thesis, security evaluations are performed using random numbers and frequently updated session keys and secret keys for well-known vulnerabilities that can occur in IoT environments and sensor devices such as sniffing, spoofing, device mutual authentication, And safety. In addition, it is confirmed that it is superior in security and key management through comparison with existing smart home security protocol.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.249-254
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• 2019

According to the recent expansion of the network environment, the spread of smart devices is continuously increasing. With the spread of smart devices such as smart phones, smart pads and wearables, changes are taking place in smart technologies and IT convergence technologies. The development of smart technology is a key element of the 4th industrial technology. The Fourth Industrial Revolution expanded the new service-based industry by adding intelligence to residential, industrial and production environments using IT convergence and smart devices. Research on providing various services using smart technologies, such as smart home, smart factory, smart farm, and smart healthcare, is being conducted in variety. In particular, There is a sharp rise in smart homes due to the proliferation of IoT devices and the growth of sensor technology, control technology, applications, data management, and cloud services. Smart home services using smart technology provide residents with convenient, beneficial services and environments. Smart home service has complemented the existing home network service, but there still are flaws to be modified. In other words, the spread of smart devices, the development of service provider-oriented services, and the interlocking of services have limitations in providing services in consideration of user environment and user state. In order to solve this problem, this study proposes a smart home service system that considers the situation of the elderly.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.277-284
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• 2021

Recently, the smart home environment is expected to be a platform that collects, integrates, and utilizes various data through convergence with wireless information and communication technology. In fact, the number of smart devices with various sensors is increasing inside smart homes. The amount of data that needs to be processed by the increased number of smart devices is also increasing, and big data processing systems are actively being introduced to handle it effectively. However, traditional big data processing systems have all requests directed to cluster drivers before they are allocated to distributed nodes, leading to reduced cluster-wide performance sharing as cluster drivers managing segmentation tasks become bottlenecks. In particular, there is a greater delay rate on smart home devices that constantly request small data processing. Thus, in this paper, we design a Apriori-based big data system for effective data processing in smart home environments where frequent requests occur at the same time. According to the performance evaluation results of the proposed system, the data processing time was reduced by up to 38.6% from at least 19.2% compared to the existing system. The reason for this result is related to the type of data being measured. Because the amount of data collected in a smart home environment is large, the use of cache servers plays a major role in data processing, and association analysis with Apriori algorithms stores highly relevant sensor data in the cache.