• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.2
• /
• pp.267-277
• /
• 2022

Due to the increasing proportion of cloud and remote working environments, various information security incidents are occurring. Insider threats have emerged as a major issue, with cases in which corporate insiders attempting to leak confidential data by accessing it remotely. In response, insider threat detection approaches based on machine learning have been developed. However, existing machine learning methods used to detect insider threats do not take biases and variances into account, which leads to limited performance. In this paper, boosting-type ensemble learning algorithms are applied to verify the performance of malicious insider detection, conduct a close analysis, and even consider the imbalance in datasets to determine the final result. Through experiments, we show that using ensemble learning achieves similar or higher accuracy to other existing malicious insider detection approaches while considering bias-variance tradeoff. The experimental results show that ensemble learning using bagging and boosting methods reached an accuracy of over 98%, which improves malicious insider detection performance by 5.62% compared to the average accuracy of single learning models used.

• Journal of Korean Society for Geospatial Information Science
• /
• v.21 no.2
• /
• pp.63-69
• /
• 2013

Change detection technique is essential to various applications of Very High-Resolution(VHR) satellite imagery and land monitoring. However, change detection accuracy of VHR satellite imagery can be decreased due to various geometrical dissimilarity. In this paper, the existing fusion evaluation indexes were revised and applied to improve VHR imagery based change detection accuracy between multi-temporal images. In addition, appropriate change detection methodology of VHR images are proposed through comparison of general change detection algorithm with cross-sharpened image based change detection algorithm. For these purpose, ERGAS, UIQI and SAM, which were representative fusion evaluation index, were applied to unsupervised change detection, and then, these were compared with CVA based change detection result. Methodologies for minimizing the geometrical error of change detection algorithm are analyzed through evaluation of change detection accuracy corresponding to image fusion method, also. The experimental results are shown that change detection accuracy based on ERGAS index by using cross-sharpened images is higher than these based on other estimation index by using general fused image.

• Journal of Korean Society of societal Security
• /
• v.1 no.4
• /
• pp.73-81
• /
• 2008

Electromagnetic induction surveys are one of the useful methods to detect the location and buried depth of underground utilities by measuring horizontal and vertical magnetic fields. It can effectively detects single buried utility with the accuracy of within 20 cm. However when another utility is buried near to target one, the accuracy of utility location considerably decreases due to the distortion of magnetic fields caused from adjacent utility. This study shows the ways to verify the location and buried depth of target utility when magnetic fields does not show symmetric distribution due to adjacent another utility. Using Bluetooth wireless communication tools, we developed the way to records measured magnetic fields to handheld PDA. We investigated the criteria for minimum distance of two adjacent utilities to separate the individual responses through field model test.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2019.06a
• /
• pp.259-261
• /
• 2019

본 논문에서는 RGB-IR 이미징 센서가 탑재된 드론을 사용하여 태양광 발전소의 태양광(PV) 패널을 탐지하는 방법을 제안한다. 태양광 발전소에서 드론에 설치된 IR 영상의 활용은 PV 패널의 결함 여부를 판단하는데 큰 도움이 된다. 그러나 IR 영상만을 사용해서 태양광 패널을 탐지하고 결함 여부를 판단하는 것은 태양광에 의해 생긴 정반사로 인해 정확도가 떨어진다. 본 논문에서 제안하는 시스템은 드론을 이용해서 IR 영상과 RGB 영상을 동시에 획득하고 활용하는 시스템을 제안한다. 제안된 시스템으로부터 IR 영상과 RGB 영상으로 패널 탐지의 정확도를 향상시키고, 태양광에 의한 정반사와 같이 오검출 될 수 있는 문제를 극복할 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.1
• /
• pp.248-254
• /
• 2013

In location finding system using spaced multi-sensor, there is the phenomenon that the position estimation accuracy is degraded by the location of signal sources and the sensors. This phenomenon is called GDOP(Geometric Dilution Of Precision) effect. and to minimize these effects, research is needed on how. In this paper, I will describe how to minimize GDOP effect, estimating possibility of GDOP using AOA(angle of arrival) information of spaced multi sensors, and removing sensor error factor in position estimation.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.6
• /
• pp.627-635
• /
• 2017

Modern warfare is gradually changing into a network war, and information electronic warfare is also progressing. In modern war, electronic warfare is all military activity concerned with electromagnetic field use, such as signal collecting, communication monitoring, information analysis, and electronic attack. The one key function of signal collecting for enemy signal analysis, direction finding, collects the signal radiated from enemy area and then calculates the enemy direction. This paper examined the Watson-Watt algorithm for an amplitude direction finding system and CVDF algorithm for phase direction finding system and analyzed the difference in the direction finding accuracy between in the clean electromagnetic field environment and in the real operating field environment of electronic warfare system. In the real field, the direction finding accuracy was affected by the reflected field from the surrounding obstacles. Therefore, this paper proposesan enhanced direction finding process for reducing the effect. The result of direction finding by applying the proposed process was enhanced above $1.24^{\circ}$ compared to the result for the existing process.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_2
• /
• pp.765-779
• /
• 2022

This study evaluates the accuracy in identifying the burned area in South Korea using multi-temporal data from Sentinel-2 MSI and Landsat 8/9 OLI. Spectral indices such as the Difference Normalized Burn Ratio (dNBR), Relative Difference Normalized Burn Ratio (RdNBR), and Burned Area Index (BAI) were used to identify the burned area in the March 2022 forest fire in Uljin. Based on the results of six indices, the accuracy to detect the burned area was assessed for four satellites using Sentinel-2 and Landsat 8/9, respectively. Sentinel-2 and Landsat 8/9 produce images every 16 and 10 days, respectively, although it is difficult to acquire clear images due to clouds. Furthermore, using images taken before and after a forest fire to examine the burned area results in a rapid shift because vegetation growth in South Korea began in April, making it difficult to detect. Because Sentinel-2 and Landsat 8/9 images from February to May are based on the same date, this study is able to compare the indices with a relatively high detection accuracy and gets over the temporal resolution limitation. The results of this study are expected to be applied in the development of new indices to detect burned areas and indices that are optimized to detect South Korean forest fires.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.2
• /
• pp.703-711
• /
• 2016

IDS (Intrusion Detection System) is used to detect network attacks through network data analysis. The system requires a high accuracy and detection rate, and low false alarm rate. In addition, the system uses a range of techniques, such as expert system, data mining, and state transition analysis to analyze the network data. The purpose of this study was to compare the performance of two data mining methods for detecting network attacks. They are Support Vector Machine (SVM) and a neural network called Forward Additive Neural Network (FANN). The well-known KDD Cup 99 training and test data set were used to compare the performance of the two algorithms. The accuracy, detection rate, and false alarm rate were calculated. The FANN showed a slightly higher false alarm rate than the SVM, but showed a much higher accuracy and detection rate than the SVM. Considering that treating a real attack as a normal message is much riskier than treating a normal message as an attack, it is concluded that the FANN is more effective in intrusion detection than the SVM.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.38 no.3
• /
• pp.187-196
• /
• 2020

In this study, collapsed building detection using UAV (Unmanned Aerial Vehicle) and PlanetScope satellite images was carried out, suggesting the possibility of utilization of heterogeneous sensors in object detection located on the surface. To this end, the area where about 20 buildings collapsed due to forest fire damage was selected as study site. First of all, the feature information of objects such as ExG (Excess Green), GLCM (Gray-Level Co-Occurrence Matrix), and DSM (Digital Surface Model) were generated using high-resolution UAV images performed object-based segmentation to detect collapsed buildings. The features were then used to detect candidates for collapsed buildings. In this process, a result of the change detection using PlanetScope were used together to improve detection accuracy. More specifically, the changed pixels acquired by the bitemporal PlanetScope images were used as seed pixels to correct the misdetected and overdetected areas in the candidate group of collapsed buildings. The accuracy of the detection results of collapse buildings using only UAV image and the accuracy of collapse building detection result when UAV and PlanetScope images were used together were analyzed through the manually dizitized reference image. As a result, the results using only UAV image had 0.4867 F1-score, and the results using UAV and PlanetScope images together showed that the value improved to 0.8064 F1-score. Moreover, the Kappa coefficiant value was also dramatically improved from 0.3674 to 0.8225.

• Journal of Korean Society of Forest Science
• /
• v.81 no.4
• /
• pp.346-356
• /
• 1992

The extensive Snake River floodplain in Northwest United States has experienced major changes in water channels and vegetation types due to floodings. To detect the change of wetland cover-types for the period of 1985 and 1988, post-classification comparison and image differencing change detection techniques were evaluated using Landsat-5 TM digital data. Differenced infrared-band images indicated better accuracy indices than any visible-band images. A thresholding technique was applied to identify the change and no change categories from the transformed images produced by image differencing. The problems in using different accuracy indices, including the Kappa coefficient of agreement, overall accuracy, producer's accuracy, user's accuracy, and average accuracy(based on both the producer's and user's accuracy approaches) in determining an optimal threshold level, were examined.