• Title/Summary/Keyword: Incremental Training

Search Result 52, Processing Time 0.023 seconds

A Study on Developing Intrusion Detection System Using APEX : A Collaborative Research Project with Jade Solution Company (APEX 기반 침입 탐지 시스템 개발에 관한 연구 : (주)제이드 솔류션과 공동 연구)

  • Kim, Byung-Joo
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
    • /
    • v.10 no.1
    • /
    • pp.38-45
    • /
    • 2017
  • Attacking of computer and network is increasing as information processing technology heavily depends on computer and network. To prevent the attack of system and network, host and network based intrusion detection system has developed. But previous rule based system has a lot of difficulties. For this reason demand for developing a intrusion detection system which detects and cope with the attack of system and network resource in real time. In this paper we develop a real time intrusion detection system which is combination of APEX and LS-SVM classifier. Proposed system is for nonlinear data and guarantees convergence. While real time processing system has its advantages, such as memory efficiency and allowing a new training data, it also has its disadvantages of inaccuracy compared to batch way. Therefore proposed real time intrusion detection system shows similar performance in accuracy compared to batch way intrusion detection system, it can be deployed on a commercial scale.

The Effect of Sampling Intervals on VO2 Plateau and Reinvestigation of Other Criteria During VO2max Test. (최대산소섭취량 측정 시 측정간격에 따른 정체현상의 변화와 종료기준의 적용)

  • Yoon, Byung-Kon;Kwak, Yi-Sub
    • Journal of Life Science
    • /
    • v.17 no.9 s.89
    • /
    • pp.1255-1259
    • /
    • 2007
  • The purpose of this study was to elucidate incidence of $VO_2$ plateau by comparing data derived from different time averaging intervals during incremental cycling exercise to $VO_2max$. Seventeen subjects (age: $23.5{\pm}3.3$ years and $VO_2max$: $3.65{\pm}0.73$ L/min, respectively) completed $VO_2max$ tests on cycle ergometer which breath by breath gas ex-change data were obtained. These data were time-averaged into 11-breath, 15, 30 and 60 s sampling intervals. The incidence of plateau were 100, 35, 24 and 6% for the 11 breath, 15 s, 30 s and 60 s averaging, respectively. No correlation was between ${\Delta}$ $VO-2$ at $VO_2max$ and $VO_2max$ (r=0.008). 53% (maximal HR within 10 b/min) and 100% $(RER{\geq}1.15)$ of subjects met the criteria for attainment of $VO_2max$. This data indicate that shorter sampling intervals (11 breath) is the most suitable for de-tection of the $VO_2$ plateau and RER can be used for the criteria for attainment of $VO_2max$ but not maximal HR. Also, the incidence of a plateau is not related to training status or physical fitness of subjects.

Face Recognition Using a Phase Difference for Images (영상의 위상 차를 이용한 얼굴인식)

  • Kim, Seon-Jong;Koo, Tak-Mo;Sung, Hyo-Kyung;Choi, Heung-Moon
    • Journal of the Korean Institute of Telematics and Electronics S
    • /
    • v.35S no.6
    • /
    • pp.81-87
    • /
    • 1998
  • This paper proposes an efficient face recognition system using phase difference between the face images. We use a Karhunen-Loeve transform for image compression and reconstruction, and obtain the phase difference by using normalized inner product of the two compressed images. The proposed system is rotation and light-invariant due to using the normalized phase difference, and somewhat shift-invariant due to applying the cosine function. The faster recognition than the conventional system and incremental training is possible in the proposed system. Simulations are conducted on the ORL images of 40 persons, in which each person has 10 facial images, and the result shows that the faster recognition than conventional recognizer using convolution network under the same recognition error rate of 8% does.

  • PDF

A dominant hyperrectangle generation technique of classification using IG partitioning (정보이득 분할을 이용한 분류기법의 지배적 초월평면 생성기법)

  • Lee, Hyeong-Il
    • Journal of the Korea Society of Computer and Information
    • /
    • v.19 no.1
    • /
    • pp.149-156
    • /
    • 2014
  • NGE(Nested Generalized Exemplar Method) can increase the performance of the noisy data at the same time, can reduce the size of the model. It is the optimal distance-based classification method using a matching rule. NGE cross or overlap hyperrectangles generated in the learning has been noted to inhibit the factors. In this paper, We propose the DHGen(Dominant Hyperrectangle Generation) algorithm which avoids the overlapping and the crossing between hyperrectangles, uses interval weights for mixed hyperrectangles to be splited based on the mutual information. The DHGen improves the classification performance and reduces the number of hyperrectangles by processing the training set in an incremental manner. The proposed DHGen has been successfully shown to exhibit comparable classification performance to k-NN and better result than EACH system which implements the NGE theory using benchmark data sets from UCI Machine Learning Repository.

Real-time Classification of Internet Application Traffic using a Hierarchical Multi-class SVM

  • Yu, Jae-Hak;Lee, Han-Sung;Im, Young-Hee;Kim, Myung-Sup;Park, Dai-Hee
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.4 no.5
    • /
    • pp.859-876
    • /
    • 2010
  • In this paper, we propose a hierarchical application traffic classification system as an alternative means to overcome the limitations of the port number and payload based methodologies, which are traditionally considered traffic classification methods. The proposed system is a new classification model that hierarchically combines a binary classifier SVM and Support Vector Data Descriptions (SVDDs). The proposed system selects an optimal attribute subset from the bi-directional traffic flows generated by our traffic analysis system (KU-MON) that enables real-time collection and analysis of campus traffic. The system is composed of three layers: The first layer is a binary classifier SVM that performs rapid classification between P2P and non-P2P traffic. The second layer classifies P2P traffic into file-sharing, messenger and TV, based on three SVDDs. The third layer performs specialized classification of all individual application traffic types. Since the proposed system enables both coarse- and fine-grained classification, it can guarantee efficient resource management, such as a stable network environment, seamless bandwidth guarantee and appropriate QoS. Moreover, even when a new application emerges, it can be easily adapted for incremental updating and scaling. Only additional training for the new part of the application traffic is needed instead of retraining the entire system. The performance of the proposed system is validated via experiments which confirm that its recall and precision measures are satisfactory.

Model for Maximum Power Point Tracking Using Artificial Neural Network and Fuzzy (인공 신경망과 퍼지를 이용한 최대 전력점 추적을 위한 모델)

  • Kim, Tae-Oh;Ha, Eun-Gyu;Kim, Chang-Bok
    • The Journal of Korean Institute of Information Technology
    • /
    • v.17 no.9
    • /
    • pp.19-30
    • /
    • 2019
  • Photovoltaic power generation requires MPPT algorithm to track stable and efficient maximum power output power point according to external changes such as solar radiation and temperature. This study implemented a model that could track MPP more quickly than original MPPT algorithm using artificial neural network. The proposed model finds the current and voltage of MPP using the original MPPT algorithm for various combinations of insolation and temperature for training data of artificial neural networks. The acquired MPP data was learned using the input node as insolation and temperature and the output node as the current and voltage. The Experiment results show tracking time of the original algorithms P&O, InC and Fuzzy were respectively 0.428t, 0.49t and 0.4076t for the 0t~0.3t range, and MPP tracking time of the proposed model was 0.32511t and it is 0.1t faster than the original algorithms.

Prediction of stress intensity factor range for API 5L grade X65 steel by using GPR and MPMR

  • Murthy, A. Ramachandra;Vishnuvardhan, S.;Saravanan, M.;Gandhi, P.
    • Structural Engineering and Mechanics
    • /
    • v.81 no.5
    • /
    • pp.565-574
    • /
    • 2022
  • The infrastructures such as offshore, bridges, power plant, oil and gas piping and aircraft operate in a harsh environment during their service life. Structural integrity of engineering components used in these industries is paramount for the reliability and economics of operation. Two regression models based on the concept of Gaussian process regression (GPR) and Minimax probability machine regression (MPMR) were developed to predict stress intensity factor range (𝚫K). Both GPR and MPMR are in the frame work of probability distribution. Models were developed by using the fatigue crack growth data in MATLAB by appropriately modifying the tools. Fatigue crack growth experiments were carried out on Eccentrically-loaded Single Edge notch Tension (ESE(T)) specimens made of API 5L X65 Grade steel in inert and corrosive environments (2.0% and 3.5% NaCl). The experiments were carried out under constant amplitude cyclic loading with a stress ratio of 0.1 and 5.0 Hz frequency (inert environment), 0.5 Hz frequency (corrosive environment). Crack growth rate (da/dN) and stress intensity factor range (𝚫K) values were evaluated at incremental values of loading cycle and crack length. About 70 to 75% of the data has been used for training and the remaining for validation of the models. It is observed that the predicted SIF range is in good agreement with the corresponding experimental observations. Further, the performance of the models was assessed with several statistical parameters, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Efficiency (E), Root Mean Square Error to Observation's Standard Deviation Ratio (RSR), Normalized Mean Bias Error (NMBE), Performance Index (ρ) and Variance Account Factor (VAF).

Bankruptcy prediction using an improved bagging ensemble (개선된 배깅 앙상블을 활용한 기업부도예측)

  • Min, Sung-Hwan
    • Journal of Intelligence and Information Systems
    • /
    • v.20 no.4
    • /
    • pp.121-139
    • /
    • 2014
  • Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

Hierarchical Internet Application Traffic Classification using a Multi-class SVM (다중 클래스 SVM을 이용한 계층적 인터넷 애플리케이션 트래픽의 분류)

  • Yu, Jae-Hak;Lee, Han-Sung;Im, Young-Hee;Kim, Myung-Sup;Park, Dai-Hee
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.20 no.1
    • /
    • pp.7-14
    • /
    • 2010
  • In this paper, we introduce a hierarchical internet application traffic classification system based on SVM as an alternative overcoming the uppermost limit of the conventional methodology which is using the port number or payload information. After selecting an optimal attribute subset of the bidirectional traffic flow data collected from the campus, the proposed system classifies the internet application traffic hierarchically. The system is composed of three layers: the first layer quickly determines P2P traffic and non-P2P traffic using a SVM, the second layer classifies P2P traffics into file-sharing, messenger, and TV, based on three SVDDs. The third layer makes specific classification of the entire 16 application traffics. By classifying the internet application traffic finely or coarsely, the proposed system can guarantee an efficient system resource management, a stable network environment, a seamless bandwidth, and an appropriate QoS. Also, even a new application traffic is added, it is possible to have a system incremental updating and scalability by training only a new SVDD without retraining the whole system. We validate the performance of our approach with computer experiments.

SIEM System Performance Enhancement Mechanism Using Active Model Improvement Feedback Technology (능동형 모델 개선 피드백 기술을 활용한 보안관제 시스템 성능 개선 방안)

  • Shin, Youn-Sup;Jo, In-June
    • The Journal of the Korea Contents Association
    • /
    • v.21 no.12
    • /
    • pp.896-905
    • /
    • 2021
  • In the field of SIEM(Security information and event management), many studies try to use a feedback system to solve lack of completeness of training data and false positives of new attack events that occur in the actual operation. However, the current feedback system requires too much human inputs to improve the running model and even so, those feedback from inexperienced analysts can affect the model performance negatively. Therefore, we propose "active model improving feedback technology" to solve the shortage of security analyst manpower, increasing false positive rates and degrading model performance. First, we cluster similar predicted events during the operation, calculate feedback priorities for those clusters and select and provide representative events from those highly prioritized clusters using XAI (eXplainable AI)-based event visualization. Once these events are feedbacked, we exclude less analogous events and then propagate the feedback throughout the clusters. Finally, these events are incrementally trained by an existing model. To verify the effectiveness of our proposal, we compared three distinct scenarios using PKDD2007 and CSIC2012. As a result, our proposal confirmed a 30% higher performance in all indicators compared to that of the model with no feedback and the current feedback system.