• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.1-10
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• 2019

Since big-data text mining extracts many features and data, clustering and classification can result in high computational complexity and low reliability of the analysis results. In particular, a term document matrix obtained through text mining represents term-document features, but produces a sparse matrix. We designed an advanced genetic algorithm (GA) to extract features in text mining for detection model. Term frequency inverse document frequency (TF-IDF) is used to reflect the document-term relationships in feature extraction. Through a repetitive process, a predetermined number of features are selected. And, we used the sparsity score to improve the performance of detection model. If a spam mail data set has the high sparsity, detection model have low performance and is difficult to search the optimization detection model. In addition, we find a low sparsity model that have also high TF-IDF score by using s(F) where the numerator in fitness function. We also verified its performance by applying the proposed algorithm to text classification. As a result, we have found that our algorithm shows higher performance (speed and accuracy) in attack mail classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.462-469
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• 2019

According to the survey of Ministry of the Interior and Safety in Korea, The number of public and private CCTV reached over ten million and is still increasing. Also with improving Image Processing Technology, it is possible to obtain diverse information. Recently, various services using CCTV are being provided. Therefore it is necessary to ensure CCTV image integrity. However there is no system to prove events in film yet. In this paper, we suggest system model that can manage, use and authenticate CCTV. This model allows a CCTV film to be verified by other nearby CCTVs' data. This model ensures film's integrity by using blockchain. And also, It addresses privacy problem in CCTV and file size problem in blockchain by using not large film data but much smaller analyzed data.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.22-25
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• 2022

본 연구는 대화형 인공지능이 비윤리적 표현을 학습하거나 생성하는 것을 방지하기 위한 기초적 연구로, 메신저 대화에 나타나는 단어 단위, 구 단위 이상의 비윤리적 표현을 수집하고 그 특성을 분석하였다. 비윤리적 표현은 '욕설, 혐오 및 차별 표현, 공격적 표현, 성적 표현'이 해당된다. 메신저 대화에 나타난 비윤리적 표현은 욕설이 가장 많은 비중을 차지했는데, 욕설에서는 비표준형뿐만 아니라 '존-', '미치다' 등과 같이 맥락을 고려하여 판단해야 하는 경우가 있다. 가장 높은 빈도로 나타난 욕설 '존나류, 씨발류, 새끼류'의 타입-토큰 비율(TTR)을 확인한 결과 '새끼류'의 TTR이 가장 높게 나타났다. 다음으로 메신저 대화에서는 공격적 표현이나 성적인 표현에 비해 혐오 및 차별 표현의 비중이 높았는데, '국적/인종'과 '젠더' 관련된 혐오 및 차별 표현이 특히 높게 나타났다. 혐오 및 차별 표현은 단어 단위보다는 구 단위 이상의 표현의 비중이 높았고 문장 단위로 떨어지기 보다는 대화 전체에 걸쳐 나타나는 것을 확인하였다. 따라서 혐오 및 차별 표현을 탐지하기 위해서는 단어 단위보다는 구 단위 이상 표현의 탐지에 대한 필요성이 있음을 학인하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.349-352
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• 2008

본 논문에서는 위치기반서비스(LBS : Location-Based Service)를 관광 분야에 접목해 모바일 단말기를 통하여 관광 정보를 제공하는 관광 정보 시스템에서 지능적인 맞춤형 컨텐츠를 제공하여주는 관광 정보 서비스 시스템을 구현하였다. 관광 정보 서비스 시스템에서는 사용자가 컨텐츠의 일정 반경에 들어가게 되면, 상세 정보를 출력하게 되는데 이때, 데이터마이닝 기법의 하나인 클러스터링을 이용하여 맞춤형 컨텐츠를 출력하게 된다. 시스템은 분석된 데이터를 이용하여 사용자의 입력에 가중치를 부여하고 그 가중치로 사용자의 선호도에 알맞은 컨텐츠를 출력한다. 이를 통해 사용자에게 맞는 맞춤형 컨텐츠를 제공한다.

• Electronics and Telecommunications Trends
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• v.29 no.4
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• pp.72-81
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• 2014

최근 CPS(Cyber-Physical Systems)는 IoT(Internet of Things), 빅데이터 기술과 함께 미래 전력산업의 핵심 키워드로 등장하고 있다. CPS는 산업의 중심이 하드웨어에서 소프트웨어로 빠르게 전환하고 있는 현시점에서 기존의 물리시스템 혹은 물리프로세스를 효율적이고 안전하며 지능적으로 만들고 운영하기 위한 기술이다. 지난 2007년 미국을 중심으로 기술개발이 시작된 이래 다시금 새로운 전환기를 맞이하고 있다. 미국은 지난 2013년 제 2기 PIF(Presidential Innovation Fellow)의 주도로 SmartAmerica Challenge라는 이름으로 새로운 가치와 일자리 창출을 위한 국민생활 밀착형 대규모 CPS 융합 프로그램을 추진 중이다. 이를 통해 미국은 학술적이고 이론적인 CPS R&D 전략으로부터 보다 현실적으로 국부를 창출할 수 있는 개방형 R&BD형의 CPS 기술발전을 모색하고 있다. 본고에서는 CPS의 태동 배경으로부터 최근 SmartAmerica Challenge에 이르는 전반적인 CPS 기술동향을 조망하고 향후 우리의 나아갈 바를 제언하는 것으로 글을 맺고자 한다.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.133-141
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• 2021

The development and demand for the system that provides users with traffic information and efficient road use have continued. also, this system provides the basic technology of the Intelligent Transport System (ITS). The most used traffic information collection tools are Vehicle detectors (VDS) and short-range wireless communication (DSRC) on express way. In order to generate reliable traffic information, it is necessary to efficiently manage and utilize the collected data as well as high-quality traffic data collection and processing technology. In this study, traffic information collection·processing·provision systems were investigated, and analyze the current status and problems of traffic information collected through VDS. Based on this, we would like to present an improved collection algorithm that utilizes the Kalman filter for vehicle information measurement of VDS data. By using the algorithm of this study, it is possible to minimize the time delay of the estimated value as well as the noise removal that inevitably occurs during measurement.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.14-23
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• 2021

The Army is making efforts to increase combat power by incorporating technologies related to the Fourth Industrial Revolution into the field of defense. In order to utilize these technologies, it is necessary to develop a military tactical communication system that enables transmission and reception of data between command and control system and weapon systems. Therefore, in this paper, we analyze the tactical communication systems of the other countries, derive the limitations of the tactical communication system currently operating in the military. And, a multi-layered integrated operation structure centered on satellites and plans to provide communication on the move to small units are reviewed. Then, we present the necessity of a large-capacity transmission speed by predicting the amount of data that will be generated from weapon systems of the future, and a plan to efficiently manage the network using intelligent network technology.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.80-87
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• 2006

In this paper, we analyze the performance of IEEE 802.11b WLAN communication between access point(AP) and mobile equipment(ME) in 2.4 GHz band with noise and interference factors. WLAN communication at in-vehicle environment is assumed as the communication between main vehicle controller and electronic device such as sensor, ECU (Electrical Control Unit) in vehicle on telematics field for implementing wireless vehicle control system. Received interference level from other system's mobile equipment in the same band and automobile noise from each part of vehicle can be the main factors that can cause increasing error rate of control signal. With these (actors, we focus on the Eb/No the BER performance of WLAN for analyzing the characteristic of interference factors by the measured bit error rate.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.9
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• pp.22-30
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• 2011

Recently, the rising demand for intelligent video surveillance system leads to high-performance face recognition systems. The solution for low-resolution images acquired by a long-distance camera is required to overcome the distance limits of the existing face recognition systems. For that reason, this paper proposes a hardware design of an image resolution enhancement algorithm for real-time intelligent video surveillance systems. The algorithm is synthesizing a high-resolution face image from an input low-resolution image, with the help of a large collection of other high-resolution face images, called training set. When we checked the performance of the algorithm at 32bit RISC micro-processor, the entire operation took about 25 sec, which is inappropriate for real-time target applications. Based on the result, we implemented the hardware module and verified it using Xilinx Virtex-4 and ARM9-based embedded processor(S3C2440A). The designed hardware can complete the whole operation within 33 msec, so it can deal with 30 frames per second. We expect that the proposed hardware could be one of the solutions not only for real-time processing at the embedded environment, but also for an easy integration with existing face recognition system.