• Journal of the Korean Magnetics Society
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• v.18 no.2
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• pp.71-74
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• 2008

In this paper, we have measured the effect of the optical spot size, incident upon the quadrant photodetector, on the optical displacement sensitivity of the optical beam deflection technique. We have built an optical displacement detection system based on the optical beam deflection method using 3 mW He-Ne laser and measured the displacement sensitivity with changing the optical spot size on the quadrant photodetector. We have also calculated the changes in the optical displacement sensitivity as a function of the incident laser spot size by modeling a circular optical spot with constant laser intensity. Our experimental and theoretical studies show that the optical displacement sensitivity increases with the decrease in the optical spot size. This suggests that in the design of the optical motion detection systems with sub-nanometer sensitivity, the displacement sensitivity can be optimized by reducing the size of the incident optical spot on the detector.

• Journal of Korea Multimedia Society
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• v.23 no.5
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• pp.641-649
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• 2020

As the usage of mobile devices extremely increases, malicious mobile apps(applications) that target mobile users are also increasing. It is challenging to detect these malicious apps using traditional malware detection techniques due to intelligence of today's attack mechanisms. Deep learning (DL) is an alternative technique of traditional signature and rule-based anomaly detection techniques and thus have actively been used in numerous recent studies on malware detection. In order to develop DL-based defense mechanisms against intelligent malicious apps, feeding recent datasets into DL models is important. In this paper, we develop a DL-based model for detecting intelligent malicious apps using KU-CISC 2018-Android, the most up-to-date dataset consisting of benign and malicious Android apps. This dataset has hardly been addressed in other studies so far. We extract OPcode sequences from the Android apps and preprocess the OPcode sequences using an N-gram model. We then feed the preprocessed data into LSTM and apply the concept of Information Gain to improve performance of detecting malicious apps. Furthermore, we evaluate our model with numerous scenarios in order to verify the model's design and performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.1024-1030
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• 2015

Safety and security system have been internationally enhanced in a field of shipping logistics. Accordingly, techniques for safety and security have been studied steadily. The need of portable radiation detection device is increasing by the search of the container is enhanced. In this paper, we propose a study to improve the life of the system and the realization of portable radiation detection device based on Cortex-A9. Configuration of a portable radiation detection device is configured largely to an analog board and the digital platform and the sensor module. The power used in each stage of the analog board is varied. Uses a switching regulator to use various power supply thereby to generate an error result and cause the switching noise. It is proposed to reduce the power consumption reducing technique for the study.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.45-56
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• 2005

The weak foundation of the computing environment caused information leakage and hacking to be uncontrollable, Therefore, dynamic control of security threats and real-time reaction to identical or similar types of accidents after intrusion are considered to be important, h one of the solutions to solve the problem, studies on intrusion detection systems are actively being conducted. To improve the anomaly IDS using system calls, this study focuses on neural networks learning using the soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern, That Is, by changing variable length sequential system call data into a fixed iength behavior pattern using the soundex algorithm, this study conducted neural networks learning by using a backpropagation algorithm. The backpropagation neural networks technique is applied for anomaly detection of system calls using Sendmail Data of UNM to demonstrate its performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2696-2707
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• 2009

Although, a number of DBMS vendors are scrambling to extend their products to handle XML, there is a need for a lightweight, DBMS and platform-independent XML repository as well. In this paper, we describe such an XML integrated repository system, that solves the following functions : generating relational schema from XML DTDs for storage of XML documents, importing data from XML documents into relational tables, creating XML documents according to a XMLQL(XML Query Language) from data extracted from a database and synchronizing the replicated XML documents. In the XML repository systems, the efficient change detection techniques for XML documents is required to maintain the consistency of replicated XML data because the same data in the repository can be replicated between so many different XML documents. In this paper, we propose a message digest based change detection technique to maintain the consistency of replicated data between client XML documents and a XML data in XML repository systems.

• The Korean Journal of Malacology
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• v.27 no.4
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• pp.387-393
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• 2011

The objective of this study was to develop a real-time PCR method for the rapid detection and quantification of the protozoan pathogen Perkinsus olseni using a TaqMan probe. For the standard, genomic DNA was extracted from $10^5$ in vitro-cultured P. olseni trophozoites, and then 10-fold serial dilutions to the level of a single cell were prepared. To test the reliability of the technique, triplicates of genomic DNA were extracted from $5{\times}10^4$ cells and 10-fold serial dilutions to the level of 5 cells were prepared. The standards and samples were analyzed in duplicate using an $Exicycler^{TM}$ 96 real-time quantitative thermal block. For quantification, the threshold cycle ($C_T$) values of samples were compared with those obtained from standard dilutions. There was a strong linear relationship between the $C_T$ value and the log concentration of cells in the standard ($r^2$ = 0.996). Detection of DNA at a concentration as low as the equivalent of a single cell showed that the assay was sensitive enough to detect a single cell of P. olseni. The estimated number of P. olseni cells was similar to the original cell concentrations, indicating the reliability of P. olseni quantification by real-time PCR. Accordingly, the designed primers and probe may be used for the rapid detection and quantification of P. olseni from clam tissue, environmental water, and sediment samples.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.923-933
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• 2007

In this paper, we present a method for performance enhancement of the marker detection system by using SVM(Support Vector Machine) and LDA(Linear Discriminant Analysis). It converts the input image to a binary image and extracts contours of objects in the binary image. After that, it approximates the contours to a list of line segments. It finds quadrangle by using geometrical features which are extracted from the approximated line segments. It normalizes the shape of extracted quadrangle into exact squares by using the warping technique and scale transformation. It extracts feature vectors from the square image by using principal component analysis. It then checks if the square image is a marker image or a non-marker image by using a SVM classifier. After that, it computes feature vectors by using LDA for the extracted marker images. And it calculates the distance between feature vector of input marker image and those of standard markers. Finally, it recognizes the marker by using minimum distance method. Experimental results show that the proposed method achieves enhancement of recognition rate with smaller feature vectors by using LDA and it can decrease false detection errors by using SVM.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2341-2348
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• 2009

An intrusion detection technique based on host consider system call sequence or system call arguments. These two ways are suitable when system call sequence or order and length of system call arguments are out of order. However, there are two disadvantages which a false positive rate and a false negative rate are high. In this paper we propose the T-N2SCD detection model based on Time Window in order to reduce false positive rate and false negative rate. Data for using this experiment is provided from DARPA. As experimental results, the proposed model showed that the false positive rate and the false negative rate are lowest at an interval of 1000ms than at different intervals.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.65-67
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• 2018

We introduce a system that detects smoking in a specific area. The proposed system is implemented on a wireless ad hoc network consisting of Raspberry Pi boards. It is more economical owing to low-cost device than commercial smoking monitoring system and is scalable than the existing system with single Raspberry Pi. In this paper, the probability density function of carbon monoxide concentration during smoking and non-smoking is approximated as Gauusian distribution, respectively, using data measured from sensors for a long time. Based on this, a maximum likelihood detection technique is adopted to estimate the smoking status by observing the concentration of carbon monoxide. We aim at improving the reliability by estimating the smoking status using the collected values from multiple sensors connected to the ad hoc network.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.293-298
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• 2014

Detecting and understanding the changes between RDF data is crucial in the evolutionary process, synchronization system, and versioning system on the web of data. However, current researches on detecting changes still remain unsatisfactory in that they did neither consider the large scale of RDF data nor accurately produce the RDF deltas. In this paper, we propose a scalable and effective change detection using a MapReduce framework which has been used in many fields to process and analyze large volumes of data. In particular, we focus on the structure-based change detection that adopts a strategy for the comparison of blank nodes in RDF data. To achieve this, we employ a method which is composed of two MapReduce jobs. First job partitions the triples with blank nodes by grouping each triple with the same blank node ID and then computes the incoming path to the blank node. Second job partitions the triples with the same path and matchs blank nodes with the Hungarian method. In experiments, we show that our approach is more accurate and effective than the previous approach.