• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.142-148
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• 2024

It is difficult to apply authentication methods of existing wired or wireless networks to Internet of Things (IoT) devices due to their poor environment, low capacity, and low-performance processor. In particular, there are many problems in applying methods such as blockchain to the IoT environment. In this paper, edge computing is used to serve as a server that authenticates disposable templates among biometric information in an IoT environment. In this environment, we propose a lightweight and strong authentication procedure using the IoT-edge computing (IoT-EC) system based on elliptic curve cryptographic (ECC) and evaluate its safety.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.667-673
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• 2021

Unauthorized generations and sales of non-funable tokens (NFTs) without the consent of the creator is one of the biggest problems that arise in NFT Marketplaces. This study proposes a method to practically reduce the problem of NFT sales without the consent of the creator by means of authentication with social media accounts. Through the proposed method, creators who are already using social media as a means of communication and marketing for creative activities can authenticate with their own accounts. Creators who have difficulty authenticating with their own accounts will be provided with alternatives to authenticate using human networks. In addition, the proposed method of protecting creator rights was designed using a flowchart to enable development using only the public API (Application Programming Interface) provided by social media. The proposed method can protect creators' rights and reduce damage caused by NFT fraud by inducing buyers to trade NFTs of authorized sellers through social media.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.8-14
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• 2023

The majority of IoT devices already employ AIoT, however there are still numerous issues that need to be resolved before AI applications can be deployed. In order to more effectively distribute IoT edge resources, this paper propose a machine learning-based approach to managing IoT edge resources. The suggested method constantly improves the allocation of IoT resources by identifying IoT edge resource trends using machine learning. IoT resources that have been optimized make use of machine learning convolution to reliably sustain IoT edge resources that are always changing. By storing each machine learning-based IoT edge resource as a hash value alongside the resource of the previous pattern, the suggested approach effectively verifies the resource as an attack pattern in a distributed AIoT context. Experimental results evaluate energy efficiency in three different test scenarios to verify the integrity of IoT Edge resources to see if they work well in complex environments with heterogeneous computational hardware.