Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295375
M. S. Abadeh, J. Habibi
The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as intrusion detection system (IDS). The objective of this paper is to extract fuzzy classification rules for intrusion detection in computer networks. The proposed method is based on the iterative rule learning approach (IRL) to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The performance of final fuzzy classification system has been investigated using intrusion detection problem as a high-dimensional classification problem. Results show that the presented algorithm produces fuzzy rules, which can be used to construct a reliable intrusion detection system.
{"title":"Computer Intrusion Detection Using an Iterative Fuzzy Rule Learning Approach","authors":"M. S. Abadeh, J. Habibi","doi":"10.1109/FUZZY.2007.4295375","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295375","url":null,"abstract":"The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as intrusion detection system (IDS). The objective of this paper is to extract fuzzy classification rules for intrusion detection in computer networks. The proposed method is based on the iterative rule learning approach (IRL) to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The performance of final fuzzy classification system has been investigated using intrusion detection problem as a high-dimensional classification problem. Results show that the presented algorithm produces fuzzy rules, which can be used to construct a reliable intrusion detection system.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122375421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295530
B. Bedregal, H. Santos, R. Callejas-Bedregal
Triangular norms or t-norms, in short, and automorphisms are very useful to fuzzy logics in the narrow sense. However, these notions are usually limited to the set [0,1]. In this paper we will consider a generalization of the t-norm notion for arbitrary bounded lattices as a category, where these generalized t-norms are the objects, and a generalization of automorphism notion as the morphism of the category. We will prove that, this category is Cartesian and a subcategory of it is Cartesian closed. We show that the usual interval t-norms can be seen as a covariant functor for that category.
{"title":"A Generalized Class of T-norms From a Categorical Point of View","authors":"B. Bedregal, H. Santos, R. Callejas-Bedregal","doi":"10.1109/FUZZY.2007.4295530","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295530","url":null,"abstract":"Triangular norms or t-norms, in short, and automorphisms are very useful to fuzzy logics in the narrow sense. However, these notions are usually limited to the set [0,1]. In this paper we will consider a generalization of the t-norm notion for arbitrary bounded lattices as a category, where these generalized t-norms are the objects, and a generalization of automorphism notion as the morphism of the category. We will prove that, this category is Cartesian and a subcategory of it is Cartesian closed. We show that the usual interval t-norms can be seen as a covariant functor for that category.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128329274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295594
A. Azzini, S. Marrara
In the last few years authentication has become of paramount importance both on the corporate Intranets and on the global Web. While most approaches focus on the initial authentication and then no further check ensure the identity of the navigating user, in this work we present a fuzzy approach to multi-modal authentication for a trust-based, continuous identity check during Web navigation. The potentiality of such an approach for generating trust-based metadata is also discussed.
{"title":"Toward trust-based multi-modal user authentication on the Web: a fuzzy approach","authors":"A. Azzini, S. Marrara","doi":"10.1109/FUZZY.2007.4295594","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295594","url":null,"abstract":"In the last few years authentication has become of paramount importance both on the corporate Intranets and on the global Web. While most approaches focus on the initial authentication and then no further check ensure the identity of the navigating user, in this work we present a fuzzy approach to multi-modal authentication for a trust-based, continuous identity check during Web navigation. The potentiality of such an approach for generating trust-based metadata is also discussed.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128448323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295423
H. Lam, C. Yeung, F. Leung
This paper presents the stability analysis and performance design for nonlinear systems. The T-S fuzzy model is employed to represent the nonlinear plant to facilitate the stability analysis. A fuzzy controller, under imperfect premise matching such that the T-S fuzzy model and the fuzzy controller do not share the same membership functions, is proposed to perform the control task. Consequently, the design flexibility can be enhanced and simple membership functions can be employed to lower the structural complexity of the fuzzy controller. However, the favourable characteristic given by perfect premise matching will vanish, which leads to conservative stability conditions. In this paper, under imperfect premise matching, the information of membership functions of the fuzzy model and controller is considered during the stability analysis. LMI-based stability conditions are derived to guarantee the system stability using the Lyapunov-based approach. Free matrices are introduced to alleviate the conservativeness of the stability conditions. LMI-based performance conditions are also derived to guarantee the system performance. Simulation examples are given to illustrate the effectiveness of the proposed approach.
{"title":"Stability Analysis and Performance Deign for Fuzzy-Model-Based Control System under Imperfect Premise Matching","authors":"H. Lam, C. Yeung, F. Leung","doi":"10.1109/FUZZY.2007.4295423","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295423","url":null,"abstract":"This paper presents the stability analysis and performance design for nonlinear systems. The T-S fuzzy model is employed to represent the nonlinear plant to facilitate the stability analysis. A fuzzy controller, under imperfect premise matching such that the T-S fuzzy model and the fuzzy controller do not share the same membership functions, is proposed to perform the control task. Consequently, the design flexibility can be enhanced and simple membership functions can be employed to lower the structural complexity of the fuzzy controller. However, the favourable characteristic given by perfect premise matching will vanish, which leads to conservative stability conditions. In this paper, under imperfect premise matching, the information of membership functions of the fuzzy model and controller is considered during the stability analysis. LMI-based stability conditions are derived to guarantee the system stability using the Lyapunov-based approach. Free matrices are introduced to alleviate the conservativeness of the stability conditions. LMI-based performance conditions are also derived to guarantee the system performance. Simulation examples are given to illustrate the effectiveness of the proposed approach.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129227524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295480
Eduard Bartl, R. Belohlávek
The present paper deals with dependencies developed within the theory of knowledge spaces. Knowledge spaces represent a new paradigm in psychological approaches to assessment of knowledge. A distinguishing feature of knowledge spaces is their non-numerical character. The aim of the present paper is twofold. First, we bring up several remarks on data dependencies studied within knowledge spaces. Second, we consider the dependencies in a framework which is more general than that of classical knowledge spaces. Namely, we abandon the assumption that a knowledge state is a set of problems/questions which an individual is able to solve. Instead, we assume that a knowledge state is a graded set (fuzzy set) of problems. Our assumption accounts for situations where it is possible that an individual can solve a particular problem partially, rather than just "can solve" or "cannot solve". We propose a definition of dependencies and validity of dependencies in knowledge spaces with graded knowledge states, provide selected properties of the dependencies, and a lemma which serves as a bridge to existing results on so-called fuzzy attribute implications.
{"title":"Knowledge Spaces, Attribute Dependencies, and Graded Knowledge States","authors":"Eduard Bartl, R. Belohlávek","doi":"10.1109/FUZZY.2007.4295480","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295480","url":null,"abstract":"The present paper deals with dependencies developed within the theory of knowledge spaces. Knowledge spaces represent a new paradigm in psychological approaches to assessment of knowledge. A distinguishing feature of knowledge spaces is their non-numerical character. The aim of the present paper is twofold. First, we bring up several remarks on data dependencies studied within knowledge spaces. Second, we consider the dependencies in a framework which is more general than that of classical knowledge spaces. Namely, we abandon the assumption that a knowledge state is a set of problems/questions which an individual is able to solve. Instead, we assume that a knowledge state is a graded set (fuzzy set) of problems. Our assumption accounts for situations where it is possible that an individual can solve a particular problem partially, rather than just \"can solve\" or \"cannot solve\". We propose a definition of dependencies and validity of dependencies in knowledge spaces with graded knowledge states, provide selected properties of the dependencies, and a lemma which serves as a bridge to existing results on so-called fuzzy attribute implications.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129847806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295471
Alexandre Evsukoff
This paper presents a method for designing fuzzy rule based systems for pattern recognition. The resulting model is interpretable as linguistic rules and can be used for deep understanding of data. The classifier performance is optimized in the least squares sense and the model complexity is minimized in a structure selection search, performed by a genetic algorithm The method is tested against benchmark classification problems found in the literature, with good results.
{"title":"Learning Fuzzy Rule Based Classifier with Rule Weights Optimization and Structure Selection by a Genetic Algorithm","authors":"Alexandre Evsukoff","doi":"10.1109/FUZZY.2007.4295471","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295471","url":null,"abstract":"This paper presents a method for designing fuzzy rule based systems for pattern recognition. The resulting model is interpretable as linguistic rules and can be used for deep understanding of data. The classifier performance is optimized in the least squares sense and the model complexity is minimized in a structure selection search, performed by a genetic algorithm The method is tested against benchmark classification problems found in the literature, with good results.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129914617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295447
Ma Nam, Nguyen T. B. Ngoc, Hoa Nguyen, T. Cao
For modelling real-world problems and constructing intelligent systems, integration of different methodologies and techniques has been the quest and focus of significant interdisciplinary research effort. The advantages of such a hybrid system are that the strengths of its partners are combined and complementary to each other's weakness. However, extended object-oriented models that combine the relevance and strength of both fuzzy set theory and probability theory appear to be sporadic. Furthermore, the soft computing paradigm needs to have real systems implemented to be useful in practice. This paper presents our development of FPDB40 as a management system for fuzzy and probabilistic object bases of the model called FPOB. The syntax and semantics of FPOB schemas, instances, and selection operation are summarized. Then the implementation of those features in FPDB40 is presented.
{"title":"FPDB40: A Fuzzy and Probabilistic Object Base Management System","authors":"Ma Nam, Nguyen T. B. Ngoc, Hoa Nguyen, T. Cao","doi":"10.1109/FUZZY.2007.4295447","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295447","url":null,"abstract":"For modelling real-world problems and constructing intelligent systems, integration of different methodologies and techniques has been the quest and focus of significant interdisciplinary research effort. The advantages of such a hybrid system are that the strengths of its partners are combined and complementary to each other's weakness. However, extended object-oriented models that combine the relevance and strength of both fuzzy set theory and probability theory appear to be sporadic. Furthermore, the soft computing paradigm needs to have real systems implemented to be useful in practice. This paper presents our development of FPDB40 as a management system for fuzzy and probabilistic object bases of the model called FPOB. The syntax and semantics of FPOB schemas, instances, and selection operation are summarized. Then the implementation of those features in FPDB40 is presented.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130707273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295460
M. Yildirim, Alper Bastürk, M. E. Yüksel
A novel filtering operator based on type-2 fuzzy logic techniques is proposed for detail preserving restoration of impulse noise corrupted images. The performance of the proposed operator is tested for different test images corrupted at various noise densities and also compared with representative conventional as well as state-of-the-art impulse noise removal operators from the literature. Experimental results show that the proposed operator exhibits superior performance over the competing operators and is capable of efficiently suppressing the noise in the image while at the same time effectively preserving the useful information in the image.
{"title":"A Detail-Preserving Type-2 Fuzzy Logic Filter for Impulse Noise Removal from Digital Images","authors":"M. Yildirim, Alper Bastürk, M. E. Yüksel","doi":"10.1109/FUZZY.2007.4295460","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295460","url":null,"abstract":"A novel filtering operator based on type-2 fuzzy logic techniques is proposed for detail preserving restoration of impulse noise corrupted images. The performance of the proposed operator is tested for different test images corrupted at various noise densities and also compared with representative conventional as well as state-of-the-art impulse noise removal operators from the literature. Experimental results show that the proposed operator exhibits superior performance over the competing operators and is capable of efficiently suppressing the noise in the image while at the same time effectively preserving the useful information in the image.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131132528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295394
A. Khosravi, Joaquim Armengol Llobet, E. Gelso
Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately.
{"title":"An Interval Intelligent-based Approach for Fault Detection and Modelling","authors":"A. Khosravi, Joaquim Armengol Llobet, E. Gelso","doi":"10.1109/FUZZY.2007.4295394","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295394","url":null,"abstract":"Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130332082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295472
J. E. M. Expósito, S. G. Galán, Nicolas Ruiz Reyes, P. V. Candeas
Automatic speech/music discrimination is an important tool used in many multimedia applications, becoming a research topic of interest in the last years. This paper presents our last works in the speech/music discrimination field, aiming to improve the coding efficiency of standard audio coders (i.e. MP3, AAC) when speech and music signals are involved. In order to discriminate between speech and music, a fuzzy rules-based expert system is incorporated into the decision-taking stage of traditional speech/music discrimination systems. The knowledge base of the fuzzy expert system has been obtained by means of a typical genetic learning algorithm (the Pittsburgh algorithm). The proposed speech/music discrimination scheme manages the operation of an intelligent audio coder, which selects a GSM coder for speech frames and an AAC coder for music ones, resulting in a lower bit rate regarding the case of using a standardized audio coder (AAC in this work). Further, the intelligent audio coder has been designed aiming to obtain a similar subjective audio quality than AAC. GSM operates at 13 kbits/s, while in the experiments the bit rate specification for AAC has been 32 kbits/s for one-channel audio signals.
{"title":"Audio Coding Improvement Using Evolutionary Speech/Music Discrimination","authors":"J. E. M. Expósito, S. G. Galán, Nicolas Ruiz Reyes, P. V. Candeas","doi":"10.1109/FUZZY.2007.4295472","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295472","url":null,"abstract":"Automatic speech/music discrimination is an important tool used in many multimedia applications, becoming a research topic of interest in the last years. This paper presents our last works in the speech/music discrimination field, aiming to improve the coding efficiency of standard audio coders (i.e. MP3, AAC) when speech and music signals are involved. In order to discriminate between speech and music, a fuzzy rules-based expert system is incorporated into the decision-taking stage of traditional speech/music discrimination systems. The knowledge base of the fuzzy expert system has been obtained by means of a typical genetic learning algorithm (the Pittsburgh algorithm). The proposed speech/music discrimination scheme manages the operation of an intelligent audio coder, which selects a GSM coder for speech frames and an AAC coder for music ones, resulting in a lower bit rate regarding the case of using a standardized audio coder (AAC in this work). Further, the intelligent audio coder has been designed aiming to obtain a similar subjective audio quality than AAC. GSM operates at 13 kbits/s, while in the experiments the bit rate specification for AAC has been 32 kbits/s for one-channel audio signals.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130625759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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