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4th international conference on Intelligent data Engineering and Automated learning (IDEAL 2003)

作者： Noh, S Lee, C Choi, K Jung, GH Catholic Univ Korea Sch Comp Sci & Informat Engn Bucheon South Korea Ajou Univ Grad Sch Informat & Commun Suwon 441749 South Korea Ajou Univ Div Elect Engn Suwon 441749 South Korea

ISBN: (纸本)354040550X

As the complexity of Internet is scaled up, it is likely for the Internet resources to be exposed to Distributed Denial of Service (DDoS) flooding attacks on TCP-based Web servers. there has been a lot of related work which focuses on analyzing the pattern of the DDoS attacks to protect users from them. However, none of these studies takes all the flags within TCP header into account, nor do they analyze relationship between the flags and the TCP packets. To analyze the features of the DDoS attacks, therefore, this paper presents a network traffic analysis mechanism which computes the ratio of the number of TCP flags to the total number of TCP packets. Based upon the calculation of TCP flag rates, we compile a pair of the TCP flag rates and the presence (or absence) of the DDoS attack into state-action rules using machine learning algorithms. We endow alarming agents with a tapestry of the compiled rules. the agents can then detect network flooding attacks against a Web server. We validate our framework with experimental results in a simulated TCP-based network setting. the experimental results show a distinctive and predictive pattern of the DDoS attacks, and our alarming agents can successfully detect various DDoS attacks.

关键词： Denial-of-service attack

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Nested state-transition graph model of user behaviors

Nested state-transition graph model of user behaviors

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4th international conference on Intelligent data Engineering and Automated learning, IDEAL 2003

作者： Heh, Jia-Sheng Cheng, Shein-Yun Ma, Nan-Chao Department of Information and Computer Engineering Chung Yuan Christian University Chung Li Taiwan

ISBN: (纸本)354040550X

this paper proposes a systematic data mining approach to study users’ Internet resource access actions for finding out behavior models as statetransition graphs. A series of Internet resource access actions are stored in a database of [user, resource-access-action, time] records. Such access actions are treated as basic behavior elements and form an action hierarchy which possesses different levels of radix codes. For every user, the data sequence is divided into a series of transactions and all the actions in a transaction constitute a special behavior pattern, called (inter-transaction) behavior. the behavior codes can be aggregated as behavior hierarchy also. Accordingly, each user can possess his/her own behavior model, formulated as a state-transition graph with behavior states and transition probability between behaviors. the overall mining process is computerized and validated by experiment. the example uses simulated sequential data to show how to combine AprioriAll algorithm and the proposed algorithm to construct a set of nested state-transition graphs. © Springer-Verlag Berlin Heidelberg 2003.

关键词： data mining

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A qualitative discriminative cohort speakers method to reduce learning data for MLP-based speaker verification systems

A qualitative discriminative cohort speakers method to reduc...

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4th international conference on Intelligent data Engineering and Automated learning, IDEAL 2003

作者： Lee, Tae-Seung Choi, Sung-Won Choi, Won-Hyuck Park, Hyeong-Taek Lim, Sang-Seok Hwang, Byong-Won Hankuk Aviation University School of Electronics Telecommunication and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)354040550X

Although multilayer perceptrons (MLPs) present several advantages against other pattern recognition methods, MLP-based speaker verification systems suffer from slow enrollment speed caused by many background speakers to achieve a low verification error. To solve this problem, the quantitative discriminative cohort speakers (QnDCS) method, by introducing the cohort speakers method into the systems, reduced the number of background speakers required to enroll speakers. Although the QnDCS achieved the goal to some extent, the improvement rate for the enrolling speed was still unsatisfactory. To improve the enrolling speed in this paper, the qualitative DCS (QlDCS) has been proposed by introducing a qualitative criterion to select less background speakers. An experiment for both methods is conducted to use the speaker verification system based on MLPs and continuants, and speech database. the results of the experiment show that the proposed QlDCS method enrolls speakers in shorter time than the QnDCS does. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Speech recognition

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An empirical comparison of in-learning and post-learning optimization schemes for tuning the support vector machines in cost-sensitive applications

An empirical comparison of in-learning and post-learning opt...

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international conference on Image Analysis and Processing

作者： F. Tortorella DAEIMI Università degli Studi di Cassino Italy

Support vector machines (SVM) are currently one of the classification systems most used in pattern recognition and data mining because of their accuracy and generalization capability. However, when dealing with very complex classification tasks where different errors bring different penalties, one should take into account the overall classification cost produced by the classifier more than its accuracy. It is thus necessary to provide some methods for tuning the SVM on the costs of the particular application. Depending on the characteristics of the cost matrix, this can be done during or after the learning phase of the classifier. In this paper we introduce two optimization schemes based on the two possible approaches and compare their performance on various data sets and kernels. the first experimental results show that both the proposed schemes are suitable for tuning SVM in cost-sensitive applications.

关键词： Support vector machines Costs Support vector machine classification pattern recognition data mining Cancer Error correction Kernel Risk management Classification algorithms

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Predicting large population data cumulative match characteristic performance from small population data

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4th international conference on Audio and Video-Based Biometric Person Authentication, AVBPA 2003

作者： Johnson, Amos Y. Sun, Jie Bobick, Aaron F. GVU Center/College of Computing Georgia Tech AtlantaGA30332 United States

ISBN: (纸本)9783540403029

Given a biometric feature-space, in this paper we present a method to predict cumulative match characteristic (CMC) curve performance for a large population of individuals using a significantly smaller population to make the prediction. this is achieved by mathematically modelling the CMC curve. For a given biometric technique that extracts measurements of individuals to be used for identification, the CMC curve shows the probability of recognizing that individual within a database of measurements that are extracted from multiple individuals. As the number of individuals in the database increase, the probabilities displayed on the CMC curve decrease, which indicate the decreasing ability of the biometric technique to recognize individuals. Our mathematical model replicates this effect, and allows us to predict the identification performance of a technique as more individuals are added without physically needing to extract measurements from more individuals. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Forecasting

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A real-time hand tracking and multimedia-based system for breast self-examination: the simplified 3D hand model

A real-time hand tracking and multimedia-based system for br...

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IEEE EMBS international conference on Information Technology Applications in Biomedicine (ITAB)

作者： Yuqin Hu R.N.G. Naguib A.G. Todman S. Amin A. Oikonomou H. Alomishy BIOCORE School of Mathematical and Information Sciences Coventry University Coventry UK Walsgrave General Hospital Coventry UK

Breast self-examination (BSE) is a non-invasive, self-administered and simple screening procedure for detecting breast cancer at an early stage. this procedure can be performed in private and at any time. A variety of leaflets and websites exist, which attempt to train women on how to perform BSE. there are also some learning systems consisting of videos and audio cassettes. However, there are no fully interactive systems in existence and no real-time feedback is given to a user on whether she is correctly performing the procedure. We aim to develop an intelligent interactive multimedia system incorporating pattern recognition and machine vision techniques, and provide real-time feedback to assist and guide women to perform BSE accurately. Using her hand in a specific configuration to conduct palpation of the breasts is the basic means available for a woman to perform BSE. However, a human hand is highly articulated and deformable with 27 degree-of-freedom parameters according to its anatomy. Recognising hand gestures and postures is a challenging task that has been studied in many areas and applications. In this paper, the simplified three-dimensional (3D) hand model is presented, which has only 8 degree-of-freedom parameters and is especially adapted for use with the breast self-examination system. this model will be a potentially effective simulation and tracking tool that will contribute to BSE learning and thus to the development of an intelligent fully interactive BSE system.

关键词： Real time systems learning systems Feedback Cancer detection Breast cancer Videos Interactive systems Intelligent systems Multimedia systems pattern recognition

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Discriminative parameter learning of general Bayesian network classifiers

Discriminative parameter learning of general Bayesian networ...

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international conference on Tools for Artificial Intelligence (ICTAI)

作者： Bin Shen Xiaoyuan Su R. Greiner P. Musilek C. Cheng Computing Science University of Alberta Edmonton AB Canada Electrical & Computer Engineering University of Alberta Edmonton AB Canada

Greiner and Zhou (1988) presented ELR, a discriminative parameter-learning algorithm that maximizes conditional likelihood (CL) for a fixed Bayesian belief network (BN) structure, and demonstrated that it often produces classifiers that are more accurate than the ones produced using the generative approach (OFE), which finds maximal likelihood parameters. this is especially true when learning parameters for incorrect structures, such as naive Bayes (NB). In searching for algorithms to learn better BN classifiers, this paper uses ELR to learn parameters of more nearly correct BN structures - e.g., of a general Bayesian network (GBN) learned from a structure-learning algorithm by Greiner and Zhou (2002). While OFE typically produces more accurate classifiers with GBN (vs. NB), we show that ELR does not, when the training data is not sufficient for the GBN structure learner to produce a good model. Our empirical studies also suggest that the better the BN structure is, the less advantages ELR has over OFE, for classification purposes. ELR learning on NB (i.e., with little structural knowledge) still performs about the same as OFE on GBN in classification accuracy, over a large number of standard benchmark datasets.

关键词： Bayesian methods Niobium Computer networks Training data Fault diagnosis machine learning Logistics Frequency estimation pattern recognition data mining

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Face verification competition on the XM2VTS database

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4th international conference on Audio and Video-Based Biometric Person Authentication, AVBPA 2003

作者： Messer, Kieron Kittler, Josef Sadeghi, Mohammad Marcel, Sebastien Marcel, Christine Bengio, Samy Cardinaux, F. Sanderson, C. Czyz, J. Vandendorpe, L. Srisuk, Sanun Petrou, Maria Kurutach, Werasak Kadyrov, Alexander Paredes, Roberto Kepenekci, B. Tek, F.B. Akar, G.B. Deravi, Farzin Mavity, Nick University of Surrey Guildford SurreyGU2 7XH United Kingdom Dalle Molle Institute for Perceptual Aritificial Intelligence CP 592 rue du Simplon 4 Martigny1920 Switzerland Universite Catholique de Louvain Batiment StevinPlace du Levant 2 Louvain-la-Neuve1348 Belgium Mahanakorn University of Technology 51 Cheum-Sampan Rd Nong Chok Bangkok10530 Thailand DSIC Universidad Politcnica de Valencia Camino de Vera s/n Valencia46022 Spain Tübitak Bilten ODTÜ Campus Ankara0653 Turkey Electronic Engineering Laboratory University of Kent CanterburyCT2 7NT United Kingdom

ISBN: (纸本)9783540403029

In the year 2000 a competition was organised to collect face verification results on an identical, publicly available data set using a standard evaluation protocol. the database used was the Xm2vts database along with the Lausanne protocol [14]. Four different institutions submitted results on the database which were subsequently published in [13]. three years later, a second contest using the same dataset and protocol was organised as part of AVBPA 2003. this time round seven seperate institutions submitted results to the competition. this paper presents the results of the competition and shows that verification results on this protocol have increased in performance by a factor of 3. © Springer-Verlag Berlin Heidelberg 2003.

关键词： database systems

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An iterative initial-points refinement algorithm for categorical data clustering

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pattern recognition LETTERS 2002年第7期23卷 875-884页

作者： Sun, Y Zhu, QM Chen, ZX Univ Nebraska Dept Comp Sci Digital Imaging & Comp Vis Lab Omaha NE 68182 USA

the original k-means clustering algorithm is designed to work primarily on numeric data sets. this prohibits the algorithm from being directly applied to categorical data clustering in many data mining applications. the k-modes algorithm [Z. Huang, Clustering large data sets with mixed numeric and categorical value, in: Proceedings of the First Pacific Asia Knowledge Discovery and data mining conference. World Scientific, Singapore, 1997, pp. 21-34] extended the k-means paradigm to cluster categorical data by using a frequency-based method to update the cluster modes versus the k-means fashion of minimizing a numerically valued cost. However, as is the case with most data clustering algorithms, the algorithm requires a pre-setting or random selection of initial points (modes) of the clusters. the differences on the initial points often lead to considerable distinct cluster results. In this paper we present an experimental study on applying Bradley and Fayyad's iterative initial-point refinement algorithm to the k-modes clustering to improve the accurate and repetitiveness of the clustering results [cf. P. Bradley, U. Fayyad, Refining initial points for k-mean clustering, in: Proceedings of the 15th international conference on machine learning, Morgan Kaufmann, Los Altos, CA, 1998]. Experiments show that the k-modes clustering algorithm using refined initial points leads to higher precision results much more reliably than the random selection method without refinement, thus making the refinement process applicable to many data mining applications with categorical data. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： data clustering pattern classification refinement algorithm data mining

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machine learning for sequential data: A review 9th

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Joint IAPR 9th international Workshop on Structural and Syntactic pattern recognition, SSPR 2002 and 4th international Workshop on Statistical Techniques in pattern recognition, SPR 2002

作者： Dietterich, thomas G. Oregon State University CorvallisOR United States

ISBN: (纸本)3540440119

Statistical learning problems in many fields involve sequential data. this paper formalizes the principal learning tasks and describes the methods that have been developed within the machine learning research community for addressing these problems. these methods include sliding window methods, recurrent sliding windows, hidden Markov models, conditional random fields, and graph transformer networks. the paper also discusses some open research issues. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Hidden Markov models

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