• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.19 no.5
• /
• pp.131-142
• /
• 2009

While users of information security products require high-quality products that are secure and have high performance, there are neither examples for evaluating the quality of information security products nor studies on the quality model and metrics for the quality evaluation. In this paper, information security products are categorized into three different types and the security and performance of various information security products are analyzed. Through this process and after consideration of information security products' security and performance, a new quality model that possesses 7 characteristics and 24 sub-characteristics has been defined. In addition, metrics consisting of 62 common and 45 extended metrics that can be used to evaluate the quality of information security products are introduced, and a proposition for a method of generating the quality evaluation metrics for specific information security products is included. The method of generating metrics proposed in this paper can be extended in order to be applied to a variety of information security products, and by generating and verifying the quality evaluation metrics for firewall, intrusion detection systems and fingerprint systems it is shown that it applicable on a variety of information security products.

• Journal of Bio-Environment Control
• /
• v.31 no.4
• /
• pp.270-278
• /
• 2022

This study was conducted using deep learning technology to classify for 'Mihwang' peach maturity with RGB images and fruit quality attributes during fruit development and maturation periods. The 730 images of peach were used in the training data set and validation data set at a ratio of 8:2. The remains of 170 images were used to test the deep learning models. In this study, among the fruit quality attributes, firmness, Hue value, and a^* value were adapted to the index with maturity classification, such as immature, mature, and over mature fruit. This study used the CNN (Convolutional Neural Networks) models for image classification; VGG16 and InceptionV3 of GoogLeNet. The performance results show 87.1% and 83.6% with Hue left value in VGG16 and InceptionV3, respectively. In contrast, the performance results show 72.2% and 76.9% with firmness in VGG16 and InceptionV3, respectively. The loss rate shows 54.3% and 62.1% with firmness in VGG16 and InceptionV3, respectively. It considers increasing for adapting a field utilization with firmness index in peach.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.3
• /
• pp.27-33
• /
• 2023

Countries around the world are calling for stronger law enforcement to combat the production and distribution of child sexual exploitation images, such as child grooming. Given the scale and importance of this social problem, it requires extensive cooperation between law enforcement, government, industry, and government organizations. In the wake of the Nth Room Case, there have been some amendments to the Enforcement Decree of the Telecommunications Business Act regarding additional telecommunications services provided by precautionary operators in Korea. While Naver and others in Korea use Electronics and Telecommunications Research Institute's own technology to filter illegal images, Microsoft uses its own PhotoDNA technology. Microsoft's PhotoDNA is so good at comparing and identifying illegal images that major global operators such as Twitter are using it to detect and filter images. In order to meet the Korean government's testing standards, Microsoft has conducted more than 16 performance tests on "PhotoDNA for Video 2.0A," which is being applied to the Bing service, in cooperation with the Korea Communications Commission and Telecommunications Technology Association. In this paper, we analyze the cases that did not pass the standards and derive improvement measures related to adding logos. In addition, we propose to use three video datasets for the performance test of filtering against illegal videos.

• Journal of Intelligence and Information Systems
• /
• v.15 no.1
• /
• pp.51-64
• /
• 2009

A location tracking sensor such as GPS, INS, Radar, and optical equipments is used in tracking Maneuvering Targets with a multi-sensor, and such systems are used to track, detect, and control UAV, guided missile, and spaceship. Until now, Most of the studies related to tracking Maneuvering Targets are on fusing multiple Radars, or adding a supplementary sensor to INS and GPS. However, A study is required to change the degree of application in fusions since the system property and error property are different from sensors. In this paper, we perform the error analysis of the sensor properties by adding a ground radar to GPS and INS for improving the tracking performance by multi-sensor fusion, and suggest the tracking algorithm that improves the precision and stability by changing the sensor probability of each sensor according to the error. For evaluation, we extract the altitude values in a simulation for the trajectory of UAV and apply the suggested algorithm to carry out the performance analysis. In this study, we change the weight of the evaluated values according to the degree of error between the navigation information of each sensor to improve the precision of navigation information, and made it possible to have a strong tracking which is not affected by external purposed environmental change and disturbance.

• Journal of the Korean Chemical Society
• /
• v.64 no.6
• /
• pp.327-333
• /
• 2020

Performance of a portable GC that can be utilized for the real time determination of volatile organic compounds in air was evaluated. It employs purified/compressed ambient air as the carrier gas eliminating the need for high pressure gas tanks. The compact system with dimensions of 35 × 26 × 15 ㎤ and weight of 5 kg is powered by either a 24 V DC external adapter or battery pack. Chromatograms of the mixture sample including benzene, toluene, ethylbenzene, and oxylene at concentrations of 1 ppmv and 20 ppmv represent a good reproducibility: 3.79% and 0.48% relative standard deviations (RSDs) for peak area variations; 0.40% and 0.08% RSDs for retention times. The method detection limit was 0.09 ppmv. A 30 m long, 0.28 mm I.D. column operated at an optimal condition yielded a peak capacity of 61 with good resolution for a 10 min isothermal analysis. The relative standard deviations (RSD) of the peak area variations and retention times during consecutive measurements over 27 h were less than 2.4%RSD and 0.5%RSD, respectively. Thus, this instrument makes it suitable for continuous and field analysis of low-concentration VOC mixtures in the indoor/outdoor environment as well as the spillage accident of hazardous chemicals.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.6
• /
• pp.1215-1225
• /
• 2021

Although Artificial Intelligence (AI) techniques have shown impressive performance in various fields, they are vulnerable to adversarial examples which induce misclassification by adding human-imperceptible perturbations to the input. Previous studies to defend the adversarial examples can be classified into three categories: (1) model retraining methods; (2) input transformation methods; and (3) adversarial examples detection methods. However, even though the defense methods against adversarial examples have constantly been proposed, there is no research to classify the type of adversarial attack. In this paper, we proposed an adversarial attack family classification method based on dimensionality reduction and clustering. Specifically, after extracting adversarial perturbation from adversarial example, we performed Linear Discriminant Analysis (LDA) to reduce the dimensionality of adversarial perturbation and performed K-means algorithm to classify the type of adversarial attack family. From the experimental results using MNIST dataset and CIFAR-10 dataset, we show that the proposed method can efficiently classify five tyeps of adversarial attack(FGSM, BIM, PGD, DeepFool, C&W). We also show that the proposed method provides good classification performance even in a situation where the legitimate input to the adversarial example is unknown.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.1D
• /
• pp.195-202
• /
• 2006

The infiltration route analysis is a military application using geospatial information technology. The result of the analysis would present vulnerable routes for potential enemy infiltration. In order to find the susceptible routes, optimal path search algorithms (Dijkstra's and $A^*$) were used to minimize the cost function, summation of detection probability. The cost function was produced by capability of TOD (Thermal Observation Device), results of viewshed analysis using DEM (Digital Elevation Model) and two related geospatial information coverages (obstacle and vegetation) extracted from VITD (Vector product Interim Terrain Data). With respect to 50m by 50m cells, the individual cost was computed and recorded, and then the optimal infiltration routes was found while minimizing summation of the costs on the routes. The proposed algorithm was experimented in Daejeon region in South Korea. The test results show that Dijkstra's and $A^*$ algorithms do not present significant differences, but A* algorithm shows a better efficiency. This application can be used for both infiltration and surveillance. Using simulation of moving TOD, the most vulnerable routes can be detected for infiltration purpose. On the other hands, it can be inversely used for selection of the best locations of TOD. This is an example of powerful geospatial solution for military application.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.10
• /
• pp.43-53
• /
• 2014

Gunnery has been used to detect and classify artilleries. In this paper, we used electro-optical data to get the information of muzzle flash from the artilleries. Feature based approach was applied; we first defined features and sub-features. The number of sub-features was 38~40 generic sub-features, and 2 model-based sub-features. To classify multiclass data, we introduced tree structure with clustering the classes according to the similarity of them. SVM was used for each non-leaf nodes in the tree, as a sub-classifier. From the data, we extracted features and sub-features and classified them by the tree structure SVM classifier. The results showed that the performance of our classifier was good for our muzzle flash classification problem.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.27 no.3
• /
• pp.307-316
• /
• 2016

A multiple radar system is comprised of early warning radar for fast detection of a target and air defense radar for precision intercept. For this reason, target take-over process is required between the two radars. The target take-over should be performed at an appropriate time by consideration of stable tracking and effective fire control. In this paper, operation characteristics of multiple radar system are analyzed and target take-over time determination method using estimation of target tracking performance is proposed for high-velocity targets. The proposed method is validated with ballistic target defense scenarios in the developed integrated simulator.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.24 no.5
• /
• pp.549-557
• /
• 2013

This paper describes a block processing approach to detect targets in front of mono-static terrain imaging radar (TIR). It is difficult to employ several conventional imaging methods of the synthetic aperture radar(SAR) because the TIR is an ultra-wide-band(UWB) type of radar and employs a dechirp-on-receive process. To design an available imaging method, a block processing approach which conducts a range compression and an azimuth compression is proposed in this paper. The complete derivation of the proposed approach is presented. The results of simulations and field tests are demonstrated to show the performance and validity of the proposed approach.