• The KIPS Transactions:PartC
• /
• v.13C no.7 s.110
• /
• pp.813-820
• /
• 2006

The advance of computer and communication technologies enables the embedded systems to be equipped with the network communication interfaces. Their appearance in network leads to security issues on the embedded systems. An easy way to overcome the security problem is to adopt the packet filter that is implemented in the general computer systems. However, general packet filters designed for host computers are not suitable to embedded systems because of their complexity. In this paper, we propose a lightweight packet filter for embedded systems. The lightweight packet filter is implemented in the kernel code. And we have installed a Web-GUI interface for user to easily set the filtering policies at remote space. The experimental results show that the proposed packet filter decreases the packet delivery time compared to the packet filter designed for host computers and it is comparable to the systems without packet filter.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.2
• /
• pp.600-616
• /
• 2021

SIMON and SPECK are two families of lightweight block ciphers that have excellent performance on hardware and software platforms. At CRYPTO 2019, Gohr first introduces the differential cryptanalysis based deep learning on round-reduced SPECK32/64, and finally reduces the remaining security of 11-round SPECK32/64 to roughly 38 bits. In this paper, we are committed to evaluating the safety of SIMON cipher under the neural differential cryptanalysis. We firstly prove theoretically that SIMON is a non-Markov cipher, which means that the results based on conventional differential cryptanalysis may be inaccurate. Then we train a residual neural network to get the 7-, 8-, 9-round neural distinguishers for SIMON32/64. To prove the effectiveness for our distinguishers, we perform the distinguishing attack and key-recovery attack against 15-round SIMON32/64. The results show that the real ciphertexts can be distinguished from random ciphertexts with a probability close to 1 only by 2^8.7 chosen-plaintext pairs. For the key-recovery attack, the correct key was recovered with a success rate of 23%, and the data complexity and computation complexity are as low as 2⁸ and 2^20.1 respectively. All the results are better than the existing literature. Furthermore, we briefly discussed the effect of different residual network structures on the training results of neural distinguishers. It is hoped that our findings will provide some reference for future research.

• Journal of IKEEE
• /
• v.24 no.4
• /
• pp.931-941
• /
• 2020

As natural language processing technology using machine learning develops, a Smart Home Network Service (SHNS) is drawing attention again. However, it is difficult to apply a standardized authentication scheme for SHNS because of the diversity of components and the variability of users. Blockchain is proposed for data authentication in a distributed environment. But there is a limit to applying it to SHNS due to the computational overhead required when implementing a proof-of-work system. In this paper, a lightweight work proof system is proposed. The proposed lightweight proof-of-work system is proposed to manage block generation by controlling the work authority of the device. In addition, this paper proposes an access control scheme for SHNS.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.11
• /
• pp.4065-4083
• /
• 2021

Super-resolution can improve the clarity of low-resolution (LR) images, which can increase the accuracy of high-level compute vision tasks. Portable devices have low computing power and storage performance. Large-scale neural network super-resolution methods are not suitable for portable devices. In order to save the computational cost and the number of parameters, Lightweight image processing method can improve the processing speed of portable devices. Therefore, we propose the Enhanced Information Multiple Distillation Network (EIMDN) to adapt lower delay and cost. The EIMDN takes feedback mechanism as the framework and obtains low level features through high level features. Further, we replace the feature extraction convolution operation in Information Multiple Distillation Block (IMDB), with Ghost module, and propose the Enhanced Information Multiple Distillation Block (EIMDB) to reduce the amount of calculation and the number of parameters. Finally, coordinate attention (CA) is used at the end of IMDB and EIMDB to enhance the important information extraction from Spaces and channels. Experimental results show that our proposed can achieve convergence faster with fewer parameters and computation, compared with other lightweight super-resolution methods. Under the condition of higher peak signal-to-noise ratio (PSNR) and higher structural similarity (SSIM), the performance of network reconstruction image texture and target contour is significantly improved.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.1
• /
• pp.121-128
• /
• 2018

Internet of Things architecture connected to the Internet is a technology. However, Many paper research for the lightweight Protocol of IoT Environment. In these Paper excluded secure problem about protocol. So Light weight Protocol has weakness of secure in IoT environment. All of IoT devices need encryption algorithm and authentication message code for certain level of security. However, IoT environment is difficult to using existing security technology. For this reason, Studies for Lightweight IPsec is essential in IoT environment. For Study of Lightweight IPsec, We analyze existing protocols such as IPsec, 6LoWPAN for IEEE 802.15.4 layer and Lightweight IPsec based 6LoWPAN. The result is to be obtained for the lightweight IPsec protocols for IoT environment. This protocol can compatible with Internet network.

• ETRI Journal
• /
• v.43 no.6
• /
• pp.1113-1129
• /
• 2021

Lightweight ciphers are increasingly employed in cryptography because of the high demand for secure data transmission in wireless sensor network, embedded devices, and Internet of Things. The PRESENT algorithm as an ultralightweight block cipher provides better solution for secure hardware cryptography with low power consumption and minimum resource. This study generates the key using key rotation and substitution method, which contains key rotation, key switching, and binary-coded decimal-based key generation used in image encryption. The key rotation and substitution-based PRESENT architecture is proposed to increase security level for data stream and randomness in cipher through providing high resistance to attacks. Lookup table is used to design the key scheduling module, thus reducing the area of architecture. Field-programmable gate array (FPGA) performances are evaluated for the proposed and conventional methods. In Virtex 6 device, the proposed key rotation and substitution PRESENT architecture occupied 72 lookup tables, 65 flip flops, and 35 slices which are comparably less to the existing architecture.

• Journal of Multimedia Information System
• /
• v.8 no.4
• /
• pp.251-258
• /
• 2021

In 2019, a novel coronavirus (COVID-19) outbreak started in China and spread all over the world. The countries went into lockdown and closed their borders to minimize the spread of the virus. Shortage of testing kits and trained clinicians, motivate researchers and computer scientists to look for ways to automatically diagnose the COVID-19 patient using X-ray and ease the burden on the healthcare system. In recent years, multiple frameworks are presented but most of them are trained on a very small dataset which makes clinicians adamant to use it. In this paper, we have presented a lightweight deep learning base automatic COVID-19 detection system. We trained our model on more than 22,000 dataset X-ray samples. The proposed model achieved an overall accuracy of 96.88% with a sensitivity of 91.55%.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.4
• /
• pp.1050-1061
• /
• 2000

This paper presents a new object model, LOM(Lightweight Object Model) and an implementation method for the distributed trader in heterogeneous distributed computing environment including mobile network. Trader is third party object that enables clients to find suitable servers, which provide the most appropriate services to client in distributed environment including dynamic reconfiguration of services and servers. Trading service requires simpler and more specific object model than genetic object models which provide richer multimedia data types and semantic characteristics with complex data structures. LOM supports a new reference attribute type instead of the relationship, inheritance and composite attribute types of the general object oriented models and so LOM has simple data structures. Also in LOM, the modelling step includes specifying of the information about users and the access right to objects for security in the mobile environment and development of the distributed storage for trading service. Also, we propose and implementation method of the distributed trader, which integrates the LOM-information object model and the OMG (object Management Group) computational object model.

• The Journal of the Korea Contents Association
• /
• v.9 no.9
• /
• pp.41-50
• /
• 2009

A conventional middleware system is optimized for wired computing environments. The wireless mobile devices have several disadvantages of low-speed processors, small memory and narrow bandwidth of wireless network. To overcome those problem issues in wireless mobile environments, middleware and many middleware-related applications have to be changed of the small sized components. In this paper, we design a lightweight mobile middleware system called "LightMware" which is optimized for mobile environment and middleware applications

• Journal of Sensor Science and Technology
• /
• v.30 no.5
• /
• pp.286-289
• /
• 2021

Image classification is one of the fundamental applications of computer vision. It enables a system to identify an object in an image. Recently, image classification applications have broadened their scope from computer applications to edge devices. The convolutional neural network (CNN) is the main class of deep learning neural networks that are widely used in computer tasks, and it delivers high accuracy. However, CNN algorithms use a large number of parameters and incur high computational costs, which hinder their implementation in edge hardware devices. To address this issue, this paper proposes a lightweight image classifier that provides good accuracy while using fewer parameters. The proposed image classifier diverts the input into three paths and utilizes different scales of receptive fields to extract more feature maps while using fewer parameters at the time of training. This results in the development of a model of small size. This model is tested on the CIFAR-10 dataset and achieves an accuracy of 90% using .26M parameters. This is better than the state-of-the-art models, and it can be implemented on edge devices.