• Title/Summary/Keyword: Network validation

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Design of Validation Procedure for Certification for PKI Based Next Generation Mobile Networks (PKI 방식의 차세대 이동통신 망에 적용 가능한 인증서 검증 절차 설계)

  • Jeong, Jang-Min;Lee, Goo-Yeon
    • Journal of Industrial Technology
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    • v.22 no.A
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    • pp.95-100
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    • 2002
  • When the wireless PKI is applied to 3G/4G mobile network which requires mutual authentication among all entities, the wired PKI procedure is not feasible for validating visited network's certifications because of the wireless environmental limitations. Also, if we depend on WAP based PKI, we cannot support confidence about certification validation since the information offered from visited network is not authenticated. Therefore, in this paper we consider various and unique characteristics of mobile environment for certification validation at 3G/4G mobile networks based on wireless PKI and then propose two certification validation procedures.

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CROSS-VALIDATION OF ARTIFICIAL NEURAL NETWORK FOR LANDSLIDE SUSCEPTIBILITY ANALYSIS: A CASE STUDY OF KOREA

  • LEE SARO;LEE MOUNG-JIN;WON JOONG-SUN
    • Proceedings of the KSRS Conference
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    • 2004.10a
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    • pp.298-301
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    • 2004
  • The aim of this study is to cross-validate of spatial probability model, artificial neural network at Boun, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the Boun, Janghung and Youngin areas from interpretation of aerial photographs, field surveys, and maps of the topography, soil type, forest cover and land use were constructed to spatial data-sets. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography, were calculated from the topographic database. Topographic type, texture, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter, age and density of forest were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using the landslide­occurrence factors by artificial neural network model. For the validation and cross-validation, the result of the analysis was applied to each study areas. The validation and cross-validate results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.

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Finding Unexpected Test Accuracy by Cross Validation in Machine Learning

  • Yoon, Hoijin
    • International Journal of Computer Science & Network Security
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    • v.21 no.12spc
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    • pp.549-555
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    • 2021
  • Machine Learning(ML) splits data into 3 parts, which are usually 60% for training, 20% for validation, and 20% for testing. It just splits quantitatively instead of selecting each set of data by a criterion, which is very important concept for the adequacy of test data. ML measures a model's accuracy by applying a set of validation data, and revises the model until the validation accuracy reaches on a certain level. After the validation process, the complete model is tested with the set of test data, which are not seen by the model yet. If the set of test data covers the model's attributes well, the test accuracy will be close to the validation accuracy of the model. To make sure that ML's set of test data works adequately, we design an experiment and see if the test accuracy of model is always close to its validation adequacy as expected. The experiment builds 100 different SVM models for each of six data sets published in UCI ML repository. From the test accuracy and its validation accuracy of 600 cases, we find some unexpected cases, where the test accuracy is very different from its validation accuracy. Consequently, it is not always true that ML's set of test data is adequate to assure a model's quality.

Improvement of Neural Network Performance for Estimating Defect Size of Steam Generator Tube using Multifold Cross-Validation (다중겹 교차검증 기법을 이용한 증기세관 결함크기 예측을 위한 신경회로망 성능 향상)

  • Kim, Nam-Jin;Jee, Su-Jung;Jo, Nam-Hoon
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.26 no.9
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    • pp.73-79
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    • 2012
  • In this paper, we study on how to determine the number of hidden layer neurons in neural network for predicting defect size of steam generator tube. It was reported in the literature that the number of hidden layer neurons can be efficiently determined with the help of cross-validation. Although the cross-validation provides decent estimation performance in most cases, the performance depends on the selection of validation set and rather poor performance may be led to in some cases. In order to avoid such a problem, we propose to use multifold cross-validation. Through the simulation study, it is shown that the estimation performance of defect width (defect depth, respectively) attains 94% (99.4%, respectively) of the best performance achievable among the considered neuron numbers.

Machine Learning Based Hybrid Approach to Detect Intrusion in Cyber Communication

  • Neha Pathak;Bobby Sharma
    • International Journal of Computer Science & Network Security
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    • v.23 no.11
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    • pp.190-194
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    • 2023
  • By looking the importance of communication, data delivery and access in various sectors including governmental, business and individual for any kind of data, it becomes mandatory to identify faults and flaws during cyber communication. To protect personal, governmental and business data from being misused from numerous advanced attacks, there is the need of cyber security. The information security provides massive protection to both the host machine as well as network. The learning methods are used for analyzing as well as preventing various attacks. Machine learning is one of the branch of Artificial Intelligence that plays a potential learning techniques to detect the cyber-attacks. In the proposed methodology, the Decision Tree (DT) which is also a kind of supervised learning model, is combined with the different cross-validation method to determine the accuracy and the execution time to identify the cyber-attacks from a very recent dataset of different network attack activities of network traffic in the UNSW-NB15 dataset. It is a hybrid method in which different types of attributes including Gini Index and Entropy of DT model has been implemented separately to identify the most accurate procedure to detect intrusion with respect to the execution time. The different DT methodologies including DT using Gini Index, DT using train-split method and DT using information entropy along with their respective subdivision such as using K-Fold validation, using Stratified K-Fold validation are implemented.

A Study on the Realtime Cert-Validation of Certification based on DARC (DARC 기반에서의 실시간 인증서 유효성 검증에 관한 연구)

  • 장홍종;이정현
    • Proceedings of the CALSEC Conference
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    • 2001.08a
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    • pp.155-163
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    • 2001
  • There are cases that revoke the certification because of disclosure of private key, deprivation of qualification and the expiration of a term of validity based on PKI. So, a user have to confirm the public key whether valid or invalid in the certification. There are many method such as CRL, Delta-CRL, OCSP for the cert-validation of certification. But these method many problems which are overload traffic on network and the CRL server because of processing for cert-validation of certification. In this paper we proposed the realtime cert-validation of certification method which solved problems that are data integrity by different time between transmission and receiving for CRL, and overload traffic on network and the CRL server based on DARC.

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Traffic Classification Using Machine Learning Algorithms in Practical Network Monitoring Environments (실제 네트워크 모니터링 환경에서의 ML 알고리즘을 이용한 트래픽 분류)

  • Jung, Kwang-Bon;Choi, Mi-Jung;Kim, Myung-Sup;Won, Young-J.;Hong, James W.
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.33 no.8B
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    • pp.707-718
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    • 2008
  • The methodology of classifying traffics is changing from payload based or port based to machine learning based in order to overcome the dynamic changes of application's characteristics. However, current state of traffic classification using machine learning (ML) algorithms is ongoing under the offline environment. Specifically, most of the current works provide results of traffic classification using cross validation as a test method. Also, they show classification results based on traffic flows. However, these traffic classification results are not useful for practical environments of the network traffic monitoring. This paper compares the classification results using cross validation with those of using split validation as the test method. Also, this paper compares the classification results based on flow to those based on bytes. We classify network traffics by using various feature sets and machine learning algorithms such as J48, REPTree, RBFNetwork, Multilayer perceptron, BayesNet, and NaiveBayes. In this paper, we find the best feature sets and the best ML algorithm for classifying traffics using the split validation.

DEVELOPMENT OF A NETWORK-BASED TRACTION CONTROL SYSTEM, VALIDATION OF ITS TRACTION CONTROL ALGORITHM AND EVALUATION OF ITS PERFORMANCE USING NET-HILS

  • Ryu, J.;Yoon, M.;SunWoo, M.
    • International Journal of Automotive Technology
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    • v.7 no.6
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    • pp.687-695
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    • 2006
  • This paper presents a network-based traction control system(TCS), where several electric control units(ECUs) are connected by a controller area network(CAN) communication system. The control system consists of four ECUs: the electric throttle controller, the transmission controller, the engine controller and the traction controller. In order to validate the traction control algorithm of the network-based TCS and evaluate its performance, a Hardware-In-the-Loop Simulation(HILS) environment was developed. Herein we propose a new concept of the HILS environment called the network-based HILS(Net-HILS) for the development and validation of network-based control systems which include smart sensors or actuators. In this study, we report that we have designed a network-based TCS, validated its algorithm and evaluated its performance using Net-HILS.

A Study on the Realtime Cert-Validation of Certification based on DARC (DARC 기반에서의 실시간 인증서 유효성 검증에 관한 연구)

  • Jang, Heung-Jong;Lee, Seong-Eun;Lee, Jeong-Hyeon
    • The KIPS Transactions:PartC
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    • v.8C no.5
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    • pp.517-524
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    • 2001
  • There are cases that revoke the certification because of disclosure of private key, deprivation of qualification and the expiration of a term of validity based on PKI. So, a user have to confirm the public key whether valid or invalid in the certification. There are many method such as CRL, Delta-CRL, OCSP for the cert-validation of certification. But these method many problems which are overload traffic on network and the CRL server because of processing for cert-validation of certification. In this paper we proposed the realtime cert-validation of certification method which solved problems that are data integrity by different time between transmission and receiving for CRL, and overload traffic on network and the CRL server based on DARC.

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Performance Improvement of Cert-Validation of Certification based on FM Subcarrier Broadcasting (FM방식을 이용한 인증서 유효성 검증의 성능 향상)

  • 장홍종;이성은;이정현
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.12 no.3
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    • pp.3-13
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    • 2002
  • There are cases that revoke the certification because of disclosure of private key, deprivation of qualification and the expiration of a term of validity on PKI. So, a user has to confirm the public key whether valid or invalid in the certification. There are many methods such as CRL, Delta-CRL, OCSP for the cert-validation of certification. But these methods have many problems, which cause overload traffic on network and the CRL server because of realtime processing for cert-validation of certification. In this paper we proposed cert-validation of certification improvement method based on FM Subcarrier Broadcasting, which solved problems that are data integrity by different time between transmission and receiving for CRL, and overload traffic on network and the CRL server the realtime management.