• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.4
• /
• pp.669-677
• /
• 2020

As the number of Internet users exploded, attacks on the web increased. In addition, the attack patterns have been diversified to bypass existing defense techniques. Traditional web firewalls are difficult to detect attacks of unknown patterns.Therefore, the method of detecting abnormal behavior by artificial intelligence has been studied as an alternative. Specifically, attempts have been made to apply natural language processing techniques because the type of script or query being exploited consists of text. However, because there are many unknown words in scripts and queries, natural language processing requires a different approach. In this paper, we propose a new classification model which uses byte pair encoding (BPE) technology to learn the embedding vector, that is often used for web attack payloads, and uses an attention mechanism-based Bi-GRU neural network to extract a set of tokens that learn their order and importance. For major web attacks such as SQL injection, cross-site scripting, and command injection attacks, the accuracy of the proposed classification method is about 0.9990 and its accuracy outperforms the model suggested in the previous study.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2003.05a
• /
• pp.165-170
• /
• 2003

This paper presents an integrated approach to unconstrained face recognition in arbitrary scenes. The front end of the system comprises of a scale and pose tolerant face detector. Scale normalization is achieved through novel combination of a skin color segmentation and log-polar mapping procedure. Principal component analysis is used with the multi-view approach proposed in[10] to handle the pose variations. For a given color input image, the detector encloses a face in a complex scene within a circular boundary and indicates the position of the nose. Next, for recognition, a radial grid mapping centered on the nose yields a feature vector within the circular boundary. As the width of the color segmented region provides an estimated size for the face, the extracted feature vector is scale normalized by the estimated size. The feature vector is input to a trained neural network classifier for face identification. The system was evaluated using a database of 20 person's faces with varying scale and pose obtained on different complex backgrounds. The performance of the face recognizer was also quite good except for sensitivity to small scale face images. The integrated system achieved average recognition rates of 87% to 92%.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.9
• /
• pp.2032-2037
• /
• 2011

Marker genes are defined as genes in which the expression level characterizes a specific experimental condition. Such genes in which the expression levels differ significantly between different groups are highly informative relevant to the studied phenomenon. In this paper, first the system can detect marker genes that are selected by ranking genes according to statistics after normalizing data with methods that are the most widely used among several normalization methods proposed the while, And it compare and analyze a performance of each of normalization methods with mult-perceptron neural network layer. The Result that apply Multi-Layer perceptron algorithm at Microarray data set including eight of marker gene that are selected using ANOVA method after Lowess normalization represent the highest classification accuracy of 99.32% and the lowest prediction error estimate.

• The KIPS Transactions:PartD
• /
• v.13D no.1 s.104
• /
• pp.133-138
• /
• 2006

Electric commerce of surpassing the traditional one appeared before the public and has currently led the change in the management of enterprises. To establish and maintain good relations with customers, electric commerce has various channels for customers that understand what they want to and suggest it to them. The bulletin board and e-mail among em are inbound information that enterprises can directly listen to customers' opinions and are different from other channels in characters. Enterprises can effectively manage the bulletin board and e-mail by understanding customers' ideas as many as possible and provide them with optimum answers. It is one of the important factors to improve the reliability of the notice board and e-mail as well as the whole electric commerce. Therefore this thesis researches into methods to classify various kinds of documents automatically in electric commerce; they are possible to solve existing problems of the bulletin board and e-mail, to operate effectively and to manage systematically. Moreover, it researches what the most suitable algorithm is in the automatic classification of Q&A documents by experiment the classifying performance of Naive Bayesian, TFIDF, Neural Network, k-NN

• The KIPS Transactions:PartB
• /
• v.9B no.6
• /
• pp.735-744
• /
• 2002

This paper proposes learning performance improvement of support vector machine using the kernel relaxation and the dynamic momentum. The dynamic momentum is reflected to different momentum according to current state. While static momentum is equally influenced on the whole, the proposed dynamic momentum algorithm can control to the convergence rate and performance according to the change of the dynamic momentum by training. The proposed algorithm has been applied to the kernel relaxation as the new sequential learning method of support vector machine presented recently. The proposed algorithm has been applied to the SONAR data which is used to the standard classification problems for evaluating neural network. The simulation results of proposed algorithm have better the convergence rate and performance than those using kernel relaxation and static momentum, respectively.

• Korean Journal of Remote Sensing
• /
• v.33 no.4
• /
• pp.423-436
• /
• 2017

This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model's parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size $106{\times}106pixels$, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.

• The Korean Journal of Applied Statistics
• /
• v.28 no.6
• /
• pp.1063-1073
• /
• 2015

In the long-term care insurance (LTCI) system, National Health Insurance Service (NHIS) provide counseling services for beneficiaries and their family caregivers, which help them use LTC services appropriately. The purpose of this study was to develop a Target Selection Model for the Counseling Services based on needs of beneficiaries and their family caregivers. To develope models, we used data set of total 2,000 beneficiaries and family caregivers who have used the long-term care services in their home in March 2013 and completed questionnaires. The Target Selection Model was established through various data-mining models such as logistic regression, gradient boosting, Lasso, decision-tree model, Ensemble, and Neural network. Lasso model was selected as the final model because of the stability, high performance and availability. Our results might improve the satisfaction and the efficiency for the NHIS counseling services.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.6
• /
• pp.922-934
• /
• 2015

In this paper, The classification between precipitation echo(PRE) and non-precipitation echo(N-PRE) (including ground echo and clear echo) is carried out from weather radar data using neuro-fuzzy algorithm. In order to classify between PRE and N-PRE, Input variables are built up through characteristic analysis of radar data. First, the event classifier as the first classification step is designed to classify precipitation event and non-precipitation event using input variables of RBFNNs such as DZ, DZ of Frequency(DZ_FR), SDZ, SDZ of Frequency(SDZ_FR), VGZ, VGZ of Frequency(VGZ_FR). After the event classification, in the precipitation event including non-precipitation echo, the non-precipitation echo is completely removed by the echo classifier of the second classifier step that is built as Type-2 FCM based RBFNNs. Also, parameters of classification system are acquired for effective performance using PSO(Particle Swarm Optimization). The performance results of the proposed echo classifier are compared with CZ. In the sequel, the proposed model architectures which use event classifier as well as the echo classifier of Interval Type-2 FCM based RBFNN show the superiority of output performance when compared with the conventional echo classifier based on RBFNN.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.23 no.11
• /
• pp.1434-1443
• /
• 1999

The turbulent fluctuations of temperature and two components of velocity have been measured with hot- and cold-wires in the Thermally Stratified Wind Tunnel(TSWT). Using the fin-tube heat exchanger type heaters and the neural network control algorithm, both stable ($dT/dz=109.4^{\circ}C$) and unstable ($dT/dz=-49.1^{\circ}C$) stratifications were realized. An ambient air jet was issued normally into the cross flow($U_{\infty}=1.0 m/s$) from a round nozzle(d = 6 mm) flushed at the bottom waII of the wind tunnel with the velocity ratio of $5.8(U_{jet}/U_{\infty})$. The characteristics of turbulent dispersion in the cross flow jet are found to change drastically depending on the thermal stratification. Especially, in the unstable condition, the vertical velocity fluctuation increases very rapidly at downstream of jet. The fluctuation velocity spectra and velocity-temperature cospectra along the jet centerline were obtained and compared. In the case of stable stratification, the heat flux cospectra changes Its sign from a certain point at the far field because of the restratification phenomenon. It is inferred that the main reason in the difference between the vertical heat fluxes is caused by the different length scales of the large eddy motions. The turbulent kinetic energy and scalar dissipation rates were estimated using partially non-isotropic and isotropic turbulent approximation. In the unstable case, the turbulent energy dissipation decreases more rapidly with the downstream distance than in the stable case.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.34 no.12
• /
• pp.1871-1876
• /
• 2010

The reduction of vehicle interior noise has been the main interest of NVH engineers. The driver's perception of the vehicle noise is strongly affected by the psychoacoustic characteristics of the noise and the SPL. The existing methods to evaluate the SQ for vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of subjective SQ values by neural network. However, these methods strongly depend on jury tests, this leads to difficulties. To reduce the important of the jury tests, we suggest a new method using the Mahalanobis distance for SQ evaluation. And, the optimal characteristic values that influenced the results of sound quality evaluation on the basis by main effect. Finally, we developed a new method based on the MD method to evaluate sound quality. The result of noise evaluation revealed that the sound quality could be well improved by changing the structural characteristics of the vehicle.