• International Journal of Computer Science & Network Security
• /
• 제21권7호
• /
• pp.159-168
• /
• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• 한국전자통신학회논문지
• /
• 제18권2호
• /
• pp.299-304
• /
• 2023

인공지능 기법 중에서 딥러닝은 많은 곳에서 사용되어 효과가 입증된 모델이다. 하지만, 딥러닝 모델이 모든 곳에서 효과적으로 사용되는 것은 아니다. 이번 논문에서는 회귀분석과 딥러닝 모델의 비교를 통하여 딥러닝 모델이 가지는 한계점을 보여주고, 딥러닝 모델의 효과적인 사용을 위한 가이드를 제시하고자 한다. 추가로 딥러닝 모델의 최적화를 위해 사용되는 다양한 기법 중, 많이 사용되는 데이터 정규화와 데이터 셔플링 기법을 실제 데이터를 기반으로 비교 평가하여 딥러닝 모델의 정확성과 가치를 높이기 위한 기준을 제시하고자 한다.

• 무역학회지
• /
• 제46권1호
• /
• pp.169-187
• /
• 2021

This paper analyzes the feasibility of using machine learning and deep learning methods to forecast the income and employment using the strategic industries as well as investment, export, and exchange rates. The decision tree, artificial neural network, support vector machine, and deep learning models were used to forecast the income and employment in Busan. The following were the main findings of the comparison of their predictive abilities. First, the decision tree models predict the income and employment well. The forecasting values for the income and employment appeared somewhat differently according to the depth of decision trees and several conditions of strategic industries as well as investment, export, and exchange rates. Second, since the artificial neural network models show that the coefficients are somewhat low and RMSE are somewhat high, these models are not good forecasting the income and employment. Third, the support vector machine models show the high predictive power with the high coefficients of determination and low RMSE. Fourth, the deep neural network models show the higher predictive power with appropriate epochs and batch sizes. Thus, since the machine learning and deep learning models can predict the employment well, we need to adopt the machine learning and deep learning models to forecast the income and employment.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2021년도 학술발표회
• /
• pp.97-97
• /
• 2021

Hydrometeorological variables contain highly complex system for temporal revolution and it is quite challenging to illustrate the system with a temporal linear and nonlinear models. In recent years, deep learning algorithms have been developed and a number of studies has focused to model the complex hydrometeorological system with deep learning models. In the current study, we investigated the temporal structure inside deep learning models for the hydrometeorological variables such as streamflow and climate indices. The results present a quite striking such that each hidden unit of the deep learning model presents different dependence structure and when the number of hidden units meet a proper boundary, it reaches the best model performance. This indicates that the deep dependence structure of deep learning models can be used to model selection or investigating whether the constructed model setup present efficient or not.

• Communications for Statistical Applications and Methods
• /
• 제28권4호
• /
• pp.329-338
• /
• 2021

Deep learning methods have been developed, used in various fields, and they have shown outstanding performances in many cases. Many studies predicted a daily stock return, a classic example of time-series data, using deep learning methods. We also tried to apply deep learning methods to Korea's stock market data. We used Korea's stock market index (KOSPI) and several individual stocks to forecast daily returns and directions. We compared several deep learning models with other machine learning methods, including random forest and XGBoost. In regression, long short term memory (LSTM) and gated recurrent unit (GRU) models are better than other prediction models. For the classification applications, there is no clear winner. However, even the best deep learning models cannot predict significantly better than the simple base model. We believe that it is challenging to predict daily stock return data even if we use the latest deep learning methods.

• Journal of Forest and Environmental Science
• /
• 제40권1호
• /
• pp.15-23
• /
• 2024

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approx-imately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

• 한국인터넷방송통신학회논문지
• /
• 제22권4호
• /
• pp.111-117
• /
• 2022

수십년간 매우 많은 소프트웨어 결함 예측 모델에 관한 연구들이 수행되었으며, 그들 중 기계학습 기법을 사용한 모델들이 가장 좋은 성능을 보였다. 딥러닝 기법은 기계학습 분야에서 가장 각광받는 기술이 되었지만 결함 예측 모델의 분류기로 사용된 연구는 거의 없었다. 몇몇 연구들은 모델의 입력 소스나 구문 데이터로부터 시맨틱 정보를 얻어내는데 딥러닝을 사용하였다. 본 논문은 3개 이상의 은닉층을 갖는 MLP를 이용하여 모델 구조와 하이퍼 파라미터를 변경하여 여러 모델들을 제작하였다. 모델 평가 실험 결과 MLP 기반 딥러닝 모델들은 기존 결함 예측 모델들과 Accuracy는 비슷한 성능을 보였으나 AUC는 유의미하게 더 우수한 성능을 보였다. 또한 또다른 딥러닝 모델인 CNN 모델보다도 더 나은 성능을 보였다.

• 한국안전학회지
• /
• 제34권6호
• /
• pp.50-57
• /
• 2019

In this study, various types of deep learning models that have been proposed recently are classified according to data input / output types and analyzed to find the deep learning model suitable for constructing a crack detection model. First the deep learning models are classified into image classification model, object segmentation model, object detection model, and instance segmentation model. ResNet-101, DeepLab V2, Faster R-CNN, and Mask R-CNN were selected as representative deep learning model of each type. For the comparison, ResNet-101 was implemented for all the types of deep learning model as a backbone network which serves as a main feature extractor. The four types of deep learning models were trained with 500 crack images taken from real concrete structures and collected from the Internet. The four types of deep learning models showed high accuracy above 94% during the training. Comparative evaluation was conducted using 40 images taken from real concrete structures. The performance of each type of deep learning model was measured using precision and recall. In the experimental result, Mask R-CNN, an instance segmentation deep learning model showed the highest precision and recall on crack detection. Qualitative analysis also shows that Mask R-CNN could detect crack shapes most similarly to the real crack shapes.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권4호
• /
• pp.1738-1764
• /
• 2019

With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and online advertising and so on. This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and future research directions. Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms of deep learning; Finally, we introduce the latest acceleration technology of deep learning and highlight the future work of deep learning.

• 전자통신동향분석
• /
• 제36권5호
• /
• pp.82-91
• /
• 2021

This study reviews application of data-driven anomaly detection techniques to the aviation domain. Recent advances in deep learning have inspired significant anomaly detection research, and numerous methods have been proposed. However, some of these advances have not yet been explored in aviation systems. After briefly introducing aviation safety issues, data-driven anomaly detection models are introduced. Along with traditional statistical and well-established machine learning models, the state-of-the-art deep learning models for anomaly detection are reviewed. In particular, the pros and cons of hybrid techniques that incorporate an existing model and a deep model are reviewed. The characteristics and applications of deep learning models are described, and the possibility of applying deep learning methods in the aviation field is discussed.