• Journal of the Korea Institute of Information Security & Cryptology
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• v.10 no.4
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• pp.33-46
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• 2000

IPsec is a protocol suite to protect the data communication between computers on internet and many VPNs(Virtual Private Networks) use IPsec protocol. IKE protocol is used to exchange keys in IPsec. Formal analysis method is used increasingly in computer science to increase the reliability of a system. In this paper, the IKE protocol is analyzed formally. This paper shows that IKE with Authentication with Signature and Authentication with Pre-Shared Key is safe, but Authentication with Public Key Encryption and A Revised Method of Authentication with Public Key Encryption are safe only with the assumption that a participant has the correct public key of the correspondent. To make sure that a participant has the correct public key of the correspondent, the usage of certificate is recommended.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.26-36
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• 2021

The Internet was created as a decentralized and autonomous system of interconnected computer networks used for data exchange across mutually trusted participants. The element technologies on the Internet, such as inter-domain and intra-domain routing and DNS, operated in a distributed manner. With the development of the Web, the Web has become indispensable in daily life. The existing web applications allow us to form online communities, generate private information, access big data, shop online, pay bills, post photos or videos, and even order groceries. This is what has led to centralization of the Web. This centralization is now controlled by the giant social media platforms that provide it as a service, but the original Internet was not like this. These giant companies realized that the decentralized network's huge value involves gathering, organizing, and monetizing information through centralized web applications. The centralized Web applications have heralded some major issues, which will likely worsen shortly. This study focuses on these problems and investigates blockchain's potentials for decentralized web architecture capable of improving conventional web services' critical features, including autonomous, robust, and secure decentralized processing and traceable trustworthiness in tamper-proof transactions. Finally, we review the decentralized web architecture that circumvents the main Internet gatekeepers and controls our data back from the giant social media companies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1778-1797
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• 2021

Document image binarization is an important pre-processing step in document analysis and archiving. The state-of-the-art models for document image binarization are variants of encoder-decoder architectures, such as FCN (fully convolutional network) and U-Net. Despite their success, they still suffer from three limitations: (1) reduced feature map resolution due to consecutive strided pooling or convolutions, (2) multiple scales of target objects, and (3) reduced localization accuracy due to the built-in invariance of deep convolutional neural networks (DCNNs). To overcome these three challenges, we propose an improved semantic segmentation model, referred to as DP-LinkNet, which adopts the D-LinkNet architecture as its backbone, with the proposed hybrid dilated convolution (HDC) and spatial pyramid pooling (SPP) modules between the encoder and the decoder. Extensive experiments are conducted on recent document image binarization competition (DIBCO) and handwritten document image binarization competition (H-DIBCO) benchmark datasets. Results show that our proposed DP-LinkNet outperforms other state-of-the-art techniques by a large margin. Our implementation and the pre-trained models are available at https://github.com/beargolden/DP-LinkNet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.180-194
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• 2021

For small image registration, feature-based approaches are likely to fail as feature detectors cannot detect enough feature points from low-resolution images. The classic FFT approach's prediction accuracy is high, but the registration time can be relatively long, about several seconds to register one image pair. To achieve real-time and high-precision rigid registration for small images, we apply deep neural networks for supervised rigid transformation prediction, which directly predicts the transformation parameters. We train deep registration models with rigidly transformed CIFAR-10 images and STL-10 images, and evaluate the generalization ability of deep registration models with transformed CIFAR-10 images, STL-10 images, and randomly generated images. Experimental results show that the deep registration models we propose can achieve comparable accuracy to the classic FFT approach for small CIFAR-10 images (32×32) and our LSTM registration model takes less than 1ms to register one pair of images. For moderate size STL-10 images (96×96), FFT significantly outperforms deep registration models in terms of accuracy but is also considerably slower. Our results suggest that deep registration models have competitive advantages over conventional approaches, at least for small images.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.4
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• pp.119-126
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• 2022

In this paper, we present important factors and methodologies that we have to follow for secure and efficient package management systems in a closed network. By analyzing previous works, we present several security considerations for the existing package management systems. Based on the consideration, we propose guidelines regarding the use of package management systems in the closed network. More specifically, we propose the development of new package management tools, utilization of physical storage media, utilization of local backup repositories, package updates, and downgrade batches for secure and efficient package management.

• Smart Media Journal
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• v.11 no.4
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• pp.38-45
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• 2022

Object detection models have become the current tool of choice for plant disease detection in precision agriculture. Most existing research improves the performance by ameliorating networks and optimizing the loss function. However, the data-centric part of a whole project also needs more investigation. In this paper, we proposed a systematic strategy with three different annotation methods for plant disease detection: local, semi-global, and global label. Experimental results on our paprika disease dataset show that a single class annotation with semi-global boxes may improve accuracy. In addition, we also studied the noise factor during the labeling process. An ablation study shows that annotation noise within 10% is acceptable for keeping good performance. Overall, this data-centric numerical analysis helps us to understand the significance of annotation methods, which provides practitioners a way to obtain higher performance and reduce annotation costs on plant disease detection tasks. Our work encourages researchers to pay more attention to label quality and the essential issues of labeling methods.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.878-885
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• 2022

Research in the field of computer vision based on deep learning is being actively conducted. However, deep learning-based models have vulnerabilities in adversarial attacks that increase the model's misclassification rate by applying adversarial perturbation. In particular, in the case of FGSM, it is recognized as one of the effective attack methods because it is simple, fast and has a considerable attack success rate. Meanwhile, as one of the efforts to visualize deep learning models, Grad-CAM enables visual explanation of convolutional neural networks. In this paper, I propose a method to generate adversarial examples with high attack success rate by applying Grad-CAM to FGSM. The method chooses fixels, which are closely related to labels, by using Grad-CAM and add perturbations to the fixels intensively. The proposed method has a higher success rate than the FGSM model in the same perturbation for both targeted and untargeted examples. In addition, unlike FGSM, it has the advantage that the distribution of noise is not uniform, and when the success rate is increased by repeatedly applying noise, the attack is successful with fewer iterations.

• Wind and Structures
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• v.34 no.6
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• pp.511-523
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• 2022

Detecting the icing on wind turbine blades built-in cold regions with conventional methods is always a very laborious, expensive and very difficult task. Regarding this issue, the use of smart systems has recently come to the agenda. It is quite possible to eliminate this issue by using the deep learning method, which is one of these methods. In this study, an application has been implemented that can detect icing on wind turbine blades images with visualization techniques based on deep learning using images. Pre-trained models of Resnet-50, VGG-16, VGG-19 and Inception-V3, which are well-known deep learning approaches, are used to classify objects automatically. Grad-CAM, Grad-CAM++, and Score-CAM visualization techniques were considered depending on the deep learning methods used to predict the location of icing regions on the wind turbine blades accurately. It was clearly shown that the best visualization technique for localization is Score-CAM. Finally, visualization performance analyses in various cases which are close-up and remote photos of a wind turbine, density of icing and light were carried out using Score-CAM for Resnet-50. As a result, it is understood that these methods can detect icing occurring on the wind turbine with acceptable high accuracy.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.250-258
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• 2022

The emerging scope of the Internet-of-Things (IoT) has piqued the interest of industry and academia in recent times. Therefore, security becomes the main issue to prevent the possibility of cyberattacks. Jamming attacks are threads that can affect performance and cause significant problems for IoT device. This study explores a smart jamming attack (coalition attack) in which the attackers were previously a part of the legitimate network and are now back to attack it based on the gained knowledge. These attackers regroup into a coalition and begin exchanging information about the legitimate network to launch attacks based on the gained knowledge. Our system enables jammer nodes to select the optimal transmission rates for attacks based on the attack probability table, which contains the most probable link transmission rate between nodes in the legitimate network. The table is updated constantly throughout the life cycle of the coalition. The simulation results show that a coalition of jammers can cause highly successful attacks.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.367-374
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• 2022

The acceleration of neural networks has become an important topic in the field of computer vision. An accelerator is absolutely necessary for accelerating the lightweight model. Most accelerator-supported operators focused on direct convolution operations. If the accelerator does not provide GEMM operation, it is mostly replaced by CPU operation. In this paper, we proposed an optimization technique for 2-stage tiling-based GEMM routines on VTA. We improved performance of the matrix multiplication routine by maximizing the reusability of the input matrix and optimizing the operation pipelining. In addition, we applied the proposed technique to the DarkNet framework to check the performance improvement of the matrix multiplication routine. The proposed GEMM method showed a performance improvement of more than 2.4 times compared to the non-optimized GEMM method. The inference performance of our DarkNet framework has also improved by at least 2.3 times.