• International Journal of Computer Science & Network Security
• /
• 제21권4호
• /
• pp.272-276
• /
• 2021

Presently, Online / Offline Users are facing cyber attacks every day. These cyber attacks affect user's performance, resources and various daily activities. Due to this critical situation, attention must be given to prevent such users through cyber attacks. The objective of this research paper is to improve the IDS systems by using machine learning approach to develop a hybrid model which controls the cyber attacks. This Hybrid model uses the available KDD 1999 intrusion detection dataset. In first step, Hybrid Model performs feature optimization by reducing the unimportant features of the dataset through decision tree, support vector machine, genetic algorithm, particle swarm optimization and principal component analysis techniques. In second step, Hybrid Model will find out the minimum number of features to point out accurate detection of cyber attacks. This hybrid model was developed by using machine learning algorithms like PSO, GA and ELM, which trained the system with available data to perform the predictions. The Hybrid Model had an accuracy of 99.94%, which states that it may be highly useful to prevent the users from cyber attacks.

• ETRI Journal
• /
• 제42권6호
• /
• pp.815-826
• /
• 2020

We propose a two-dimensional (2D) scattering-center-extraction (SCE) method using sparse recovery based on the compressive-sensing theory, even with data missing from the received radar cross-section (RCS) dataset. First, using the proposed method, we generate a 2D grid via adaptive discretization that has a considerably smaller size than a fully sampled fine grid. Subsequently, the coarse estimation of 2D scattering centers is performed using both the method of iteratively reweighted least square and a general peak-finding algorithm. Finally, the fine estimation of 2D scattering centers is performed using the orthogonal matching pursuit (OMP) procedure from an adaptively sampled Fourier dictionary. The measured RCS data, as well as simulation data using the point-scatterer model, are used to evaluate the 2D SCE accuracy of the proposed method. The results indicate that the proposed method can achieve higher SCE accuracy for an incomplete RCS dataset with missing data than that achieved by the conventional OMP, basis pursuit, smoothed L0, and existing discrete spectral estimation techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권12호
• /
• pp.3815-3835
• /
• 2022

Density-based outlier detection is one of the hot issues in data mining. A point is determined as outlier on basis of the density of points near them. The existing density-based detection algorithms have high time complexity, in order to reduce the time complexity, a new outlier detection algorithm DODMD (Density-based Outlier Detection in Multidimensional Datasets) is proposed. Firstly, on the basis of ZH-tree, the concept of micro-cluster is introduced. Each leaf node is regarded as a micro-cluster, and the micro-cluster is calculated to achieve the purpose of batch filtering. In order to obtain n sets of approximate outliers quickly, a greedy method is used to calculate the boundary of LOF and mark the minimum value as LOF_min. Secondly, the outliers can filtered out by LOF_min, the real outliers are calculated, and then the result set is updated to make the boundary closer. Finally, the accuracy and efficiency of DODMD algorithm are verified on real dataset and synthetic dataset respectively.

• 한국정보통신학회논문지
• /
• 제23권10호
• /
• pp.1311-1319
• /
• 2019

현재 웹 환경은 정보 공유와 비즈니스 수행을 위해 보편적으로 사용되고 있는 영역으로 개인 정보 유출이나 시스템 장애 등을 목표로 하는 외부 해킹의 공격 타켓이 되고 있다. 기존의 사이버 공격 탐지 기술은 일반적으로 시그니처 기반 분석으로 공격 패턴의 변경이 발생할 경우 탐지가 어렵다는 한계가 있다. 특히 웹 취약점 기반 공격 중 삽입 공격은 가장 빈번히 발생하는 공격이고 다양한 변형 공격이 언제든 가능하다. 본 논문에서는 웹서버 로그에서 정상상태를 벗어나는 비정상 상태를 탐지하는 이상상태 탐지 기법을 제안한다. 제안된 방법은 웹서버 로그 내 문자열 항목을 머신러닝 기반 임베딩 기법으로 벡터로 치환한 후 다수의 정상 데이터와 상이한 경향성을 보이는 비정상 데이터를 탐지하는 머신러닝 기반 이상상태 탐지 기법이다.

• Journal of Information Science Theory and Practice
• /
• 제10권spc호
• /
• pp.135-142
• /
• 2022

Recently, with the development of data processing technology and the increase of computational power, methods to solving social problems using Artificial Intelligence (AI) are in the spotlight, and AI technologies are replacing and supplementing existing traditional methods in various fields. Meanwhile in Korea, heavy rain is one of the representative factors of natural disasters that cause enormous economic damage and casualties every year. Accurate prediction of heavy rainfall over the Korean peninsula is very difficult due to its geographical features, located between the Eurasian continent and the Pacific Ocean at mid-latitude, and the influence of the summer monsoon. In order to deal with such problems, the Korea Meteorological Administration operates various state-of-the-art observation equipment and a newly developed global atmospheric model system. Nevertheless, for precipitation nowcasting, the use of a separate system based on the extrapolation method is required due to the intrinsic characteristics associated with the operation of numerical weather prediction models. The predictability of existing precipitation nowcasting is reliable in the early stage of forecasting but decreases sharply as forecast lead time increases. At this point, AI technologies to deal with spatio-temporal features of data are expected to greatly contribute to overcoming the limitations of existing precipitation nowcasting systems. Thus, in this project the dataset required to develop, train, and verify deep learning-based precipitation nowcasting models has been constructed in a regularized form. The dataset not only provides various variables obtained from multiple sources, but also coincides with each other in spatio-temporal specifications.

• 스마트미디어저널
• /
• 제12권10호
• /
• pp.19-28
• /
• 2023

과실 수확 분야에서 다양한 계절성과 수확 비용 상승 등으로 자동 로봇 수확에 대한 관심이 증가하고 있다. 빛의 변화, 바람에 의한 진동, 나뭇잎 및 가지 겹침 등 복잡한 과수원 환경에서 정확한 사과 검출은 어려운 문제이다. 본 논문에서는 로봇 자동 사과 수확에 유리한 데이터셋과 적응형 히트맵 회귀 모델을 소개한다. 사과 데이터셋은 사과 위치뿐만 아니라 가시성을 같이 레이블링하였다. 가시성에 따라 가우시안 모양을 조절하는 적응형 히트맵 회귀 모델을 사용하여 사과 중심점을 검출하는 방법을 제안한다. 실험 결과 MAP@K가 K=5와 K=10일 때 0.9809, 0.9801로 사과 수확 로봇에 응용 가능한 성능을 나타내었다.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2000년도 춘계정기학술대회 e-Business를 위한 지능형 정보기술 / 한국지능정보시스템학회
• /
• pp.235-241
• /
• 2000

This article suggests integrated neural network models for the interest rate forecasting using change point detection. The basic concept of proposed model is to obtain intervals divided by change point, to identify them as change-point groups, and to involve them in interest rate forecasting. the proposed models consist of three stages. The first stage is to detect successive change points in interest rate dataset. The second stage is to forecast change-point group with data mining classifiers. The final stage is to forecast the desired output with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. This article is then to examine the predictability of integrated neural network models for interest rate forecasting using change-point detection.

• 분석과학
• /
• 제28권4호
• /
• pp.270-277
• /
• 2015

비등점은 유기물의 물리화학적 성질을 특정하는데 있어 매우 근본적 요소 중 하나이다. 그러나 기존의 정량적 구조-물성 상관관계식들은 고에너지 물질 등과 같은 특정 물질 군에 대한 실험값들의 부족 등으로 인해 제한적인 응용성을 가지고 있었다. 본 연구에서는 서로 다른 출처로부터의 5,923개의 비등점 자료를 확보하였으며, 이에는 일반적 유기화합물과 더불어 특수목적을 가지는 분자들을 포함하였고, 이들 수집된 데이터 셋을 이용하여 새로운 비등점 예측모델을 개발하는데 사용하였다. 다양한 학습 방법을 이용하여 새로이 수집된 데이터 셋을 이용한 2차원 분자 표현자에 기반한 비등점 모델을 도출하였다. 개발된 예측모델의 적정성과 견고성을 확인하였고, 훈련 셋의 표현자에 기반한 비등점 예측모델의 적용구역을 도출하였다.

• 한국데이터정보과학회:학술대회논문집
• /
• 한국데이터정보과학회 2003년도 추계학술대회
• /
• pp.57-72
• /
• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2000년도 추계정기학술대회:지능형기술과 CRM
• /
• pp.417-426
• /
• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.