• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.14 no.3
• /
• pp.3-12
• /
• 2004

Secure operating system is a special operating system that integrates some security functions(i.e. access control, user authentication, audit-trail and etc.) with normal operating system in order to protect system from various attacks. But it doesn't consider my security of network traffic. To guarantee the security of the whole system, network traffic must be protected by a certain way and IPsec is a representative technology for network security. However, it requires administrator's carefulness in managing security policies and the key management mechanism is very heavy as well as complicated. Moreover, it doesn't have a suitable framework for delivery of security information for access control mechanism. So we propose a simple trusted channel mechanism for secure communication between secure operating systems. It provides confidentiality md authentication for network traffic and ability to deliver security information. It is implemented at the kernellevel of IP layer and the simplicity of the mechanism can minimize the overhead of trusted channel processing.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.3
• /
• pp.433-441
• /
• 2021

Android applications are released across various categories, including productivity apps and games, and users are exposed to various applications and even malware depending on their usage patterns. On the other hand, most analysis engines train using existing datasets and do not reflect user patterns even if periodic updates are made. Thus, the detection rate for known malware is high, while types of malware such as adware are difficult to detect. In addition, existing engines incur increased service provider costs due to the cost of server farm, and the user layer suffers from problems where availability and real-timeness are not guaranteed. To address these problems, we propose an analysis system that performs on-device malware detection through transfer learning, which requires only one-time communication with the server. In addition, The system has a complete process on the device, including decompiler, which can distribute the load of the server system. As an evaluation result, it shows 90.3% accuracy without transfer learning, while the model transferred with adware catergories shows 95.1% of accuracy, which is 4.8% higher compare to original model.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.6
• /
• pp.768-777
• /
• 2020

In this paper, we describe recent research results on AODV routing protocol, which is widely deployed at mobile ad hoc networks, and AODV related routing protocols with multi-path routing schemes. We suggest the critical problems which minimum hop routing schemes have, such as AODV routing protocol, and then, propose a new C-AODV routing protocol with two routing metrics: the primary metric is the hop count, the secondary metric is the sum of link delay times. We implemented C-AODV protocol by modifying AODV at the NS-3, and thus, elaborate on how we change the original AODV source code of NS-3 in order to implement the C-AODV scheme. We show numerical comparison of C-AODV scheme with the original AODV scheme and then, discuss how much the C-AODV enhances routing performance over AODV protocol. In conclusion, we present future research items.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.4
• /
• pp.533-540
• /
• 2022

Specialists diagnose skin cancer using a dermatoscopy to detect skin cancer as early as possible, but it is difficult to determine accurate skin lesions because skin lesions have various shapes. Recently, the skin lesion segmentation method using deep learning, which has shown high performance, has a problem in segmenting skin lesions because the boundary between healthy skin and skin lesions is not clear. To solve these issues, the proposed method constructs a transformer block to effectively segment the skin lesion, and constructs an edge decoder for each layer of the network to segment the skin lesion in detail. Experiment results have shown that the proposed method achieves a performance improvement of 0.041 ~ 0.071 for Dic Coefficient and 0.062 ~ 0.112 for Jaccard Index, compared with the previous method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.3
• /
• pp.333-340
• /
• 2022

The COVID-19 has made everyone anxious and people need to keep their distance. It is necessary to conduct collective assessment and screening of college students' mental health in the opening season of every year. This study uses and trains a multi-layer perceptron neural network model for deep learning to identify facial emotions. After the training, real pictures and videos were input for face detection. After detecting the positions of faces in the samples, emotions were classified, and the predicted emotional results of the samples were sent back and displayed on the pictures. The results show that the accuracy is 93.2% in the test set and 95.57% in practice. The recognition rate of Anger is 95%, Disgust is 97%, Happiness is 96%, Fear is 96%, Sadness is 97%, Surprise is 95%, Neutral is 93%, such efficient emotion recognition can provide objective data support for capturing negative. Deep learning emotion recognition system can cooperate with traditional psychological activities to provide more dimensions of psychological indicators for health.

• Journal of IKEEE
• /
• v.26 no.2
• /
• pp.287-291
• /
• 2022

In general, as the amplitude and phase information of the physical layer channel impulse response change rapidly in time and frequency according to the high-speed movement of the vehicles in V2X communication systems, it is difficult to accurately estimate these channels at the receiving end. In order to effectively overcome this problem, midamble-based channel estimation methods in which mid-ambles are periodically inserted into a packet have been recently considered. However, as the number of midambles increases, we suffer from the spectral efficiency loss. To relieve such a loss, in this paper, we propose a new bidirectional averaging channel estimation method that combines the existing data pilot-based channel estimation methods and the mid-ambles. Finally, through the simulation results, we demonstrate that the proposed method outperforms the existing mid-amble method in terms of packet error rate with fewer number of mid-ambles.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.12
• /
• pp.1835-1845
• /
• 2021

To accurately identify black ice and warn the drivers of information in advance so they can control speed and take preventive measures. In this paper, we propose a lightweight black ice detection network based on infrared road images. A black ice recognition network model based on CNN transfer learning has been developed. Additionally, to further improve the accuracy of black ice recognition, an enhanced lightweight network based on MobileNetV2 has been developed. To reduce the amount of calculation, linear bottlenecks and inverse residuals was used, and four bottleneck groups were used. At the same time, to improve the recognition rate of the model, each bottleneck group was connected to a 3×3 convolutional layer to enhance regional feature extraction and increase the number of feature maps. Finally, a black ice recognition experiment was performed on the constructed infrared road black ice dataset. The network model proposed in this paper had an accurate recognition rate of 99.07% for black ice.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.5
• /
• pp.682-687
• /
• 2022

In this paper, the research results on monolithic three-dimensional integrated-circuit (M3DICs) stacked with tunneling field effect transistors (TFETs) are introduced. Unlike metal-oxide-semiconductor field-effect transistors (MOSFETs), TFETs are designed differently from the layout of symmetrical MOSFETs because the source and drain of TFET are asymmetrical. Various monolithic 3D inverter (M3D-INV) structures and layouts are possible due to the asymmetric structure, and among them, a simple inverter structure with the minimum metal layer is proposed. Using the proposed M3D-INV, this M3D logic gates such as NAND and NOR gates by sequentially stacking TFETs are proposed, respectively. The simulation results of voltage transfer characteristics of the proposed M3D logic gates are investigated using mixed-mode simulator of technology computer aided design (TCAD), and the operation of each logic circuit is verified. The cell area for each M3D logic gate is reduced by about 50% compared to one for the two-dimensional planar logic gates.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.9
• /
• pp.1330-1339
• /
• 2022

3D object detection generally aims to detect relatively large data such as automobiles, buses, persons, furniture, etc, so it is vulnerable to small object detection. In addition, in an environment with limited resources such as embedded devices, it is difficult to apply the model because of the huge amount of computation. In this paper, the accuracy of small object detection was improved by focusing on local features using only one layer, and the inference speed was improved through the proposed knowledge distillation method from large pre-trained network to small network and adaptive quantization method according to the parameter size. The proposed model was evaluated using SUN RGB-D Val and self-made apple tree data set. Finally, it achieved the accuracy performance of 62.04% at mAP@0.25 and 47.1% at mAP@0.5, and the inference speed was 120.5 scenes per sec, showing a fast real-time processing speed.

• Journal of Sensor Science and Technology
• /
• v.30 no.4
• /
• pp.267-272
• /
• 2021

In this study, Ag was deposited to investigate its applicability as a surface-enhanced Raman scattering substrate after forming a grass-type black silicon structure through maskless reactive ion etching. Grass-structured black silicon with heights of 2 - 7 ㎛ was formed at radio-frequency (RF) power of 150 - 170 W. The process pressure was 250 mTorr, the O₂/SF₆ gas ratio was 15/37.5, and the processing time was 10 - 20 min. When the processing time was increased by more than 20 min, the self-masking of Si_xO_yF_z did not occur, and the black silicon structure was therefore not formed. Raman response characteristics were measured based on the Ag thickness deposited on a black silicon substrate. As the Ag thickness increased, the characteristic peak intensity increased. When the Ag thickness deposited on the black silicon substrate increased from 40 to 80 nm, the Raman response intensity at a Raman wavelength of 1507 / cm increased from 8.2 × 10³ to 25 × 10³ cps. When the Ag thickness was 150 nm, the increase declined to 30 × 10³ cps and showed a saturation tendency. When the RF power increased from 150 to 170 W, the response intensity at a 1507/cm Raman wavelength slightly increased from 30 × 10³ to 33 × 10³ cps. However, when the RF power was 200 W, the Raman response intensity decreased significantly to 6.2 × 10³ cps.