• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권9호
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• pp.3685-3700
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• 2015

To avoid influences caused by pose, illumination and facial expression variations, we propose a robust three-step algorithm based on ASM and POEM for facial landmark localization. Firstly, Model Selection Factor is utilized to achieve a pose-free initialized shape. Then, we use the global shape model of ASM to describe the whole face and the texture model POEM to adjust the position of each landmark. Thirdly, a second localization is presented to discriminatively refine the subtle shape variation for some organs and contours. Experiments are conducted in four main face datasets, and the results demonstrate that the proposed method accurately localizes facial landmarks and outperforms other state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권6호
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• pp.1194-1208
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• 2010

As visual surveillance systems gain wider usage in a variety of fields, it is important that they are capable of interpreting scenes automatically, also known as "human motion analysis" (HMA). However, existing HMA methods are too domain specific and computationally expensive. This paper proposes a general purpose HMA method that is based on the idea that human beings tend to exhibit erratic motion patterns during abnormal situations. Limb movements are characterized using the statistics of angular and linear displacements. In addition, the method is enhanced via the use of the entropy of the Fourier spectrum to measure the randomness of subject's motions. Various experiments have been conducted and the results indicate that the proposed method has very high classification accuracy in identifying anomalous behavior.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권11호
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• pp.2866-2879
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• 2012

Among the various security threats in online games, the use of game bots is the most serious problem. Previous studies on game bot detection have proposed many methods to find out discriminable behaviors of bots from humans based on the fact that a bot's playing pattern is different from that of a human. In this paper, we look at the chatting data that reflects gamers' communication patterns and propose a communication pattern analysis framework for online game bot detection. In massive multi-user online role playing games (MMORPGs), game bots use chatting message in a different way from normal users. We derive four features; a network feature, a descriptive feature, a diversity feature and a text feature. To measure the diversity of communication patterns, we propose lightly summarized indices, which are computationally inexpensive and intuitive. For text features, we derive lexical, syntactic and semantic features from chatting contents using text mining techniques. To build the learning model for game bot detection, we test and compare three classification models: the random forest, logistic regression and lazy learning. We apply the proposed framework to AION operated by NCsoft, a leading online game company in Korea. As a result of our experiments, we found that the random forest outperforms the logistic regression and lazy learning. The model that employs the entire feature sets gives the highest performance with a precision value of 0.893 and a recall value of 0.965.

• 인터넷정보학회논문지
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• 제8권3호
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• pp.33-43
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• 2007

본 논문에서는 하다마드 변환을 이용하여 워터마크 정보를 삽입 및 추출하는 손실압축에 강인한 워터마킹 방법을 제안한다. 하다마드 행렬은 1과 -1로 구성된 행렬이므로 계산이 빠르고 또한 역변환이 가능한 행렬이므로 워터마크 구현에 사용 가능하다. 워터마크 삽입과정에서, 워터마크는 하다마드 계수중의 중간 주파수 계수 10개를 선택하여 워터마크 패턴을 이용하여 삽입하였다. 워터마크 추출과정에서는, 삽입 시 사용했던 워터마크 패턴을 이용하여 비교하는 방법을 사용하여 워터마크 정보를 추출하였다. 실험결과, 하다마드 도메인에서 40%비트의 이진 로고영상을 삽입하였을 때 PSNR(Peok Signal To Noise Rate)이 $36{\sim}46dB$사이에서 BER(Bit Error Rate)이 $3.9{\sim}12.5%$에 달하는 성능을 나타내었고 JPEG 압축에 대해서는 QF(Quality Factor)가 30에서부터 육안으로 구분할 수 있을 정도의 로고를 추출해 낼 수 있었다. 본 논문에서는 하다마드 도메인에서의 성능을 증명하기 위하여 DCT, FFT, DWT등과 비교하여 실험한 결과 하마다드 도메인에서 가장 좋은 성능을 나타냄을 알 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권4호
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• pp.1807-1823
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• 2016

Local Binary Pattern (LBP) and its variants have powerful discriminative capabilities but most of them just consider each LBP code independently. In this paper, we propose sub oriented histograms of LBP for smoke detection and image classification. We first extract LBP codes from an image, compute the gradient of LBP codes, and then calculate sub oriented histograms to capture spatial relations of LBP codes. Since an LBP code is just a label without any numerical meaning, we use Hamming distance to estimate the gradient of LBP codes instead of Euclidean distance. We propose to use two coordinates systems to compute two orientations, which are quantized into discrete bins. For each pair of the two discrete orientations, we generate a sub LBP code map from the original LBP code map, and compute sub oriented histograms for all sub LBP code maps. Finally, all the sub oriented histograms are concatenated together to form a robust feature vector, which is input into SVM for training and classifying. Experiments show that our approach not only has better performance than existing methods in smoke detection, but also has good performance in texture classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권6호
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• pp.2513-2533
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• 2018

In cognitive radio technology, the overall efficiency of communications systems can be improved without allocating additional bands by allowing a secondary system to utilize the licensed band when the primary system, which has the right to use the band, does not use it. In this paper, we propose a fast and reliable common channel initialization protocol without any exchange of initialization messages between the cluster head and the member nodes in cognitive ad-hoc networks. In the proposed method, the cluster and member nodes perform channel-based spectrum sensing. After sensing, the cluster head transmits a system activation signal through its available channels with a predetermined angle difference pattern. To detect the cluster head's transmission channels and to join the cluster, each member node implements fast Fourier transform (FFT) and computes autocorrelation for the angle difference sequence of the received signal patterns. This is compared to the predetermined reference angle difference pattern. The join-request and channel-decision procedures are presented in this paper. Performance evaluation of the proposed method is presented in the simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권8호
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• pp.3965-3983
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• 2017

We propose a novel eye tracking method that can estimate 3D world coordinates using an infrared (IR) stereo camera for indoor and outdoor environments. This method first detects dark evidences such as eyes, eyebrows and mouths by fast multi-level thresholding. Among these evidences, eye pair evidences are detected by evidential reasoning and geometrical rules. For robust accuracy, two classifiers based on multiple layer perceptron (MLP) using gradient local binary patterns (GLBPs) verify whether the detected evidences are real eye pairs or not. Finally, the 3D world coordinates of detected eyes are calculated by region-based stereo matching. Compared with other eye detection methods, the proposed method can detect the eyes of people wearing sunglasses due to the use of the IR spectrum. Especially, when people are in dark environments such as driving at nighttime, driving in an indoor carpark, or passing through a tunnel, human eyes can be robustly detected because we use active IR illuminators. In the experimental results, it is shown that the proposed method can detect eye pairs with high performance in real-time under variable illumination conditions. Therefore, the proposed method can contribute to human-computer interactions (HCIs) and intelligent transportation systems (ITSs) applications such as gaze tracking, windshield head-up display and drowsiness detection.

• 한국멀티미디어학회논문지
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• 제13권8호
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• pp.1163-1170
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• 2010

본 논문에서는 인터넷을 이용한 원격지 공정관리 시스템을 개발하는데 있어서 유지보수가 용이하고, 기능의 추가가 쉽도록 자바 언어와 GoF 디자인 패턴을 이용한 설계기법을 보인다. 시스템의 구현을 위해 현재 운전되고 있는 설비들의 상태를 파악할 수 있도록 설비 제어 박스에 있는 PLC에 RS232C와 RS422/RS485 통신모듈을 추가하였고, PLC를 통하여 제어되고 있는 정보를 송수신하기 위해 RS232C 통신을 Ethernet으로 변환시켜주는 변환기를 설치하였다. 다계층으로 구성된 시스템을 구현하기 위해 Swing 컴포넌트를 사용하였으며, Applet과 Frame GUI를 동시에 지원하여, 관리자가 인터넷을 통하여 원격지의 작업공정 진도를 쉽게 파악할 수 있도록 하였다. 다계층 구조의 주요 목적은 클라이언트들 간의 자원을 공유하는 것이다. 제안된 시스템은 원격지에서 설비를 제어하기 위한 소프트웨어를 제작하는데 도움을 줄 수 있고, 이와 비슷한 소프트웨어를 제작하려는 개발자에게 기존 코드를 쉽게 재사용하여, 새로운 기능을 쉽게 추가할 수 있다는 장점이 있다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제11권5호
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• pp.197-202
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• 2022

IoT (Internet of Things) 장치는 취약한 아이디/비밀번호 사용, 인증되지 않은 펌웨어 업데이트 등 많은 보안 취약점을 보여 악성코드의 공격 대상이 되고 있다. 그러나 CPU 구조의 다양성으로 인해 악성코드 분석 환경 설정과 특징 설계에 어려움이 있다. 본 논문에서는 CPU 구조와 독립된 악성코드의 특징 표현을 위해 실행 파일의 바이트 순서를 이용한 시계열 특징을 설계하고 순환 신경망을 통해 분석한다. 제안하는 특징은 바이트 순서의 부분 엔트로피 계산과 선형 보간을 통한 고정 길이의 시계열 패턴이다. 추출된 특징의 시계열 변화는 RNN과 LSTM으로 학습시켜 분석한다. 실험에서 IoT 악성코드 탐지는 높은 성능을 보였지만, 패밀리 분류는 비교적 성능이 낮았다. 악성코드 패밀리별 엔트로피 패턴을 시각화하여 비교했을 때 Tsunami와 Gafgyt 패밀리가 유사한 패턴을 나타내 분류 성능이 낮아진 것으로 분석되었다. 제안된 악성코드 특징의 데이터 간 시계열 변화 학습에 RNN보다 LSTM이 더 적합하다.

• 아태비즈니스연구
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• 제13권3호
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• pp.293-309
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• 2022

Purpose - The purpose of this study is to explore the possibility of predicting the degree of smartphone overdependence based on mobile phone usage patterns. Design/methodology/approach - In this study, a survey conducted by Korea Internet and Security Agency(KISA) called "problematic smartphone use survey" was analyzed. The survey consists of 180 questions, and data were collected from 29,712 participants. Based on the data on the smartphone usage pattern obtained through the questionnaire, the smartphone addiction level was predicted using machine learning techniques. k-NN, gradient boosting, XGBoost, CatBoost, AdaBoost and random forest algorithms were employed. Findings - First, while various factors together influence the smartphone overdependence level, the results show that all machine learning techniques perform well to predict the smartphone overdependence level. Especially, we focus on the features which can be obtained from the smartphone log data (without psychological factors). It means that our results can be a basis for diagnostic programs to detect problematic smartphone use. Second, the results show that information on users' age, marriage and smartphone usage patterns can be used as predictors to determine whether users are addicted to smartphones. Other demographic characteristics such as sex or region did not appear to significantly affect smartphone overdependence levels. Research implications or Originality - While there are some studies that predict smartphone overdependence level using machine learning techniques, but the studies only present algorithm performance based on survey data. In this study, based on the information gain measure, questions that have more influence on the smartphone overdependence level are presented, and the performance of algorithms according to the questions is compared. Through the results of this study, it is shown that smartphone overdependence level can be predicted with less information if questions about smartphone use are given appropriately.