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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Kwon, Soonhong Son, Wooyoung Lee, Jong-Hyouk Sejong University Dept. of Computer and Information Security and Convergence Engineering for Intelligent Drone Seoul Korea Republic of Sejong University Protocol Engineering Lab. Seoul Korea Republic of

ISBN: (纸本)9798350333336

In this paper, we address the growing challenges arising from the rapid expansion of the global digital content market, fueled by COVID-19-induced digital transformation and increased reliance on online streaming services. While this trend has many positive implications, it has also led to persistent concerns surrounding copyright infringement and unfair distribution of copyright fees, negatively impacting copyright holders. To tackle these issues, we propose a new copyright distribution management system utilizing holographic multi-channel QR codes, which aims to safeguard the rights of copyright holders and ensure a fair distribution of royalties. © 2023 IEEE.

关键词： Copyrights

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UCI and EICN Integrated Model for Copyright Distribution Management

UCI and EICN Integrated Model for Copyright Distribution Man...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

ISBN: (纸本)9798350333336

With the recent surge in interest surrounding the metaverse environment, the significance of effective copyright license management has grown considerably. While systematic copyright management is crucial, the limitations of existing copyright identification systems pose challenges to maintaining proper control. To address these issues, this paper proposes an integrated model combining Unique Content Identifier (UCI) and Extended Integrated Copyright Number (EICN) to develop a robust copyright identification system that safeguards the rights of copyright holders. © 2023 IEEE.

关键词： Copyrights

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Trusted and Privacy-Preserving Billing System for Smart Grids

Trusted and Privacy-Preserving Billing System for Smart Grid...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Kayumov Abduaziz Shincheol Lee Ji Sun Shin Department of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul South Korea Department of Computer and Information Security Sejong University Seoul South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

With the advent of smart meters in recent years, privacy has become one of the biggest concerns in smart grids. While privacy is achieved by designing smart grid services while maintaining the anonymous participation of consumers, several technical issues must be solved by electricity companies, such as confirmation of payments or assurance of consumption data integrity and quality. In this paper, we propose a new system model for data aggregation in smart grid environments that balances the needs of consumers and electricity companies. To decentralize and achieve transparent data processing, we apply a permissioned blockchain network and smart contracts. To support consumer privacy, we propose a privacy-preserving smart contract that runs on a trusted execution environment. Payments are processed and confirmed at the end of a billing period by the smart contract to enforce data consistency and integrity. We implement our smart contract design for Hyperledger Fabric, a permissioned blockchain network, and analyze its privacy, data, and blockchain benefits.

关键词： Privacy Data privacy Electricity Data integrity Smart contracts Companies Smart meters Smart grids Blockchains Reliability

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A Self-Adaptive Framework for Responding to Uncertainty in Access Control Process with Deep Neural Networks

A Self-Adaptive Framework for Responding to Uncertainty in A...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Jihoon Park Giluk Kang Young-Gab Kim Dept. of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul Republic of Korea

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The rapid increase in Internet of Things (IoT) devices and data across various domains (e.g., healthcare, smart cities, smart factories, and smart homes) has led to growing complexity in managing security policies to protect them. Moreover, the increased complexity makes it even more challenging to define security policies in the attribute-based access control (ABAC) model, which requires considering user requirements and environmental conditions. Thus, the ABAC model is likely to include unexpected uncertainties when administrators define security policies. In this study, uncertainty refers to unforeseen future outcomes that may arise from factors the administrator has not considered. Furthermore, the increasing number of IoT devices makes it nearly impossible for administrators to predict all uncertainties and adjust security policies accordingly. Several studies have been proposed to address these challenges, but they rely on human intervention. Therefore, this study proposes a self-adaptive framework that combines a self-adaptive system with deep neural networks (DNNs) to respond to uncertainties intelligently and autonomously. The framework effectively identifies uncertainties in the access control process through variational autoencoder (VAE) and long short-term memory (LSTM) without human intervention and autonomously responds using a self-adaptive system.

关键词： Access control Privacy Uncertainty Smart cities Process control Smart homes Complexity theory Internet of Things Long short term memory Smart manufacturing

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Transfer Learning-Based Robust Insider Threat Detection

Transfer Learning-Based Robust Insider Threat Detection

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Yujun Kim Deok-Han Kim Young-Gab Kim Dept. of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul Republic of Korea

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

A malicious insider’s threats who has access to the organization’s systems and is familiar with security policies are difficult to detect and can cause significant financial damage. A reconstruction-based anomaly detection method leveraging deep learning is one method for detecting insider threats. The method is employed to solve a data imbalance problem caused by the scarcity of abnormal data in real environments. The method trains a deep learning model to reconstruct normal data received from all users. After training, the model reconstructs normal data more accurately than abnormal data, and the reconstruction-based anomaly detection method can detect anomalies based on the reconstruction difference between normal and abnormal data. However, existing reconstruction-based anomaly detection methods train on all users’ normal data using the same network parameters. Consequently, if a behavior is normal for one user but abnormal for another, the reconstruction model learns this behavior as normal because the reconstruction model trains only on normal behaviors. Since normal behaviors differ for each user depending on their position, role, and other factors within the organization, it is necessary to develop methods for detecting abnormal behaviors specific to each user. To address this problem, we propose a transfer learning-based insider threat detection method. The proposed method consists of 1) a pre-trained encoder that outputs latent representations of normal data for all users and 2) user-specific decoders assigned to each user, which are trained on the corresponding user’s normal data to reconstruct their normal data. We evaluate the proposed method’s detection rate (DR) and area under the curve (AUC) on the CERT dataset and compare it with a deep learning model trained on normal data from all users. The experimental results show that the proposed method achieves higher DR and AUC than the deep learning model. These results indicate that the proposed method en

关键词： Deep learning Training Autoencoders Transfer learning Organizations Threat assessment Data models Decoding security Anomaly detection

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A Method for Quantitative Object De-Identification Analysis of Anonymized Video

A Method for Quantitative Object De-Identification Analysis ...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Deok-Han Kim Yujun Kim Young-Gab Kim Dept. of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul Republic of Korea

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The rise in video content has amplified the risk of information leakage, prompting the development of various anonymization techniques. These techniques include traditional methods such as pixelation, blurring, and masking, as well as more advanced approaches like video encryption, which involves encrypting parameters during video encoding, and techniques that subtly alter facial features to prevent identification. However, assessing the effectiveness of these anonymization techniques remains challenging. Common metrics such as the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR), often used for object anonymization in videos, primarily assess image quality and lack accuracy for security evaluation. Furthermore, subjective human evaluations fail to provide the consistent, quantitative data necessary for cross-study comparisons. Given that a single identifiable frame in a video can compromise overall security, this study introduces a quantitative method for evaluating the de-identification rate in regions of interest (ROI) within videos. The proposed approach calculates an object de-identification rate (ODR) by combining SSIM and edge detection ratio (EDR) using a harmonic mean and a power mean. Our proposed method enhances the precision and reliability for security of anonymized video.

关键词： Measurement Data privacy Privacy PSNR Power system harmonics Video surveillance information filtering Encryption Reliability information integrity

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A Method for De-Identification Analysis of Encrypted Video

A Method for De-Identification Analysis of Encrypted Video

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Deok-Han Kim Young-Gab Kim Dept. of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul South Korea

As the variety of video content increases, so does the likelihood of data breaches of sensitive personal information. Consequently, several video de-identification techniques have been developed to protect personal data, alongside ongoing research into video encryption technology, which offers the advantage of decryption to obtain the original video without storing a separate video copy. While it is possible to visually evaluate whether individual encrypted frames have been correctly de-identified, human interpretation makes this method vulnerable to subjective bias. Many video encryption studies employ traditional image quality evaluation metrics, such as peak signal-to-noise ratio and structural similarity index measure (SSIM), to objectively demonstrate the de-identification of encrypted videos. However, these metrics prove to be insufficiently effective for this purpose. These image metrics can measure the de-identification rate of a single frame in encrypted videos, but not the entire video. This study introduces a quantitative analysis method to confirm de-identification in fully encrypted videos. In this study, we measured the de-identification rates of all frames of encrypted videos using a weighted sum of SSIM and edge differential ratio. We also measured the de-identification rate of a fully encrypted video. Finally, we graphically analyzed these data using line charts. This objective methodology enabled the identification of portions of the encrypted video where de-identification techniques may not have been appropriately applied.

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Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

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computer Systems Science & engineering 2023年第9期46卷 2741-2754页

作者： Sana Zahir Rafi Ullah Khan Mohib Ullah Muhammad Ishaq Naqqash Dilshad Amin Ullah Mi Young Lee Institute of Computer Sciences and Information Technology(ICS/IT) The University of AgriculturePeshawar25130Khyber PakhtunkhwaPakistan Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Collaborative Robotics and Intelligent Systems(CoRIS)Institute Oregon State UniversityORUSA Department of Software Sejong UniversitySeoul05006Korea

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and *** counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary *** crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test *** this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd *** proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training *** decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive *** benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

关键词： Artificial intelligence deep learning crowd counting scene understanding

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Improved Hybrid Precoding Technique with Low-Resolution for MIMO-OFDM System

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computers, Materials & Continua 2021年第8期68卷 2205-2220页

作者： Seulgi Lee Ji-Sung Jung Young-Hwan You Hyoung-Kyu Song Department of Information and Communication Engineering and Convergence for Intelligent DroneSejong UniversitySeoul05006Korea Department of Computer Engineering Sejong UniversitySeoul05006Korea

This paper proposes an improved hybrid beamforming system based on multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)*** proposed beamforming system improves energy efficiency compared to the conventional hybrid beamforming *** sub-connected and full-connected structure are considered to apply the proposed *** the conventional hybrid beamforming,the usage of radio frequency(RF)chains and phase shifter(PS)gives high power and hardware *** this paper,the phase over sampling(POS)with switches(SW)is used in hybrid beamforming system to improve the energy *** POS-SW structure samples the value of analog beamformer to make lower resolution than conventional *** number of output data in POS is decided by the resolution of POS *** limited number of POS decides the resolution of antenna array and the values of POSs are designed from maximum and minimum phase angle antenna *** efficiency without the phase shifter is high although channel capacity is nearly similar with conventional ***,the amplifier with POS-SW system is proposed to improve the BER *** to the data bits,the output signals of POS are *** system with 2,3 and 4 bits is simulated to prove the proposed *** order to overcome the loss of low-resolution system,the amplifier with POS-SW system using channel information is *** average sum-rate of 4 bits system shows the similar performance with the conventional hybrid beamforming *** structure can play an important role by increasing the energy efficiency of the wireless communication system that many antennas are *** is shown that the BER,average sum rate and energy efficiency of the proposed scheme are more improved than the conventional hybrid beamforming system.

关键词： Hybrid beamforming MIMO-OFDM system analog precoding POS-SW

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Weighted Gauss-Seidel Precoder for Downlink Massive MIMO Systems

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computers, Materials & Continua 2021年第5期67卷 1729-1745页

作者： Jun-Yong Jang Won-Seok Lee Jae-Hyun Ro Young-Hawn You Hyoung-Kyu Song Department of Information and Communication Engineering Convergence Engineering for Intelligent DroneSejong UniversitySeoul05006Korea Department of Computer Engineering Sejong UniversitySeoul05006Korea

In this paper,a novel precoding scheme based on the Gauss-Seidel(GS)method is proposed for downlink massive multiple-input multiple-output(MIMO)*** GS method iteratively approximates the matrix inversion and reduces the overall complexity of the precoding *** addition,the GS method shows a fast convergence rate to the Zero-forcing(ZF)method that requires an exact invertible ***,to satisfy demanded error performance and converge to the error performance of the ZF method in the practical condition such as spatially correlated channels,more iterations are necessary for the GS method and increase the overall *** efficient approximation with fewer iterations,this paper proposes a weighted GS(WGS)method to improve the approximation accuracy of the GS *** optimal weights that accelerate the convergence rate by improved accuracy are computed by the least square(LS)*** the computation of weights,the different weights are applied for each iteration of the GS *** addition,an efficient method of weight computation is proposed to reduce the complexity of the LS *** simulation results show that bit error rate(BER)performance for the proposed scheme with fewer iterations is better than the GS method in spatially correlated channels.

关键词： Massive MIMO GS matrix inversion complexity weight

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