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Suche nach „[D.] [Fisch]“ hat 10 Publikationen gefunden
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    F: Maschinenbau und Mechatronik

    Zeitschriftenartikel

    D. Fisch, T. Gruber, Bernhard Sick

    SwiftRule: Mining Comprehensible Classication Rules for Time Series Analysis

    IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 5, pp. 774-787

    2011

    DOI: 10.1109/TKDE.2010.161

    Abstract anzeigen

    In this article, we provide a new technique for temporal data mining which is based on classification rules that can easily be understood by human domain experts. Basically, time series are decomposed into short segments, and short-term trends of the time series within the segments (e.g., average, slope, and curvature) are described by means of polynomial models. Then, the classifiers assess short sequences of trends in subsequent segments with their rule premises. The conclusions gradually assign an input to a class. As the classifier is a generative model of the processes from which the time series are assumed to originate, anomalies can be detected, too. Segmentation and piecewise polynomial modeling are done extremely fast in only one pass over the time series. Thus, the approach is applicable to problems with harsh timing constraints. We lay the theoretical foundations for this classifier, including a new distance measure for time series and a new technique to construct a dynamic classifier from a static one, and demonstrate its properties by means of various benchmark time series, for example, Lorenz attractor time series, energy consumption in a building, or ECG data.

    F: Maschinenbau und Mechatronik

    Zeitschriftenartikel

    D. Fisch, B. Kuehlbeck, Bernhard Sick, et al.

    So Near And Yet So Far: New Insight Into Properties Of Some Well-Known Classier Paradigms

    Information Sciences, vol. 180, no. 18, pp. 3381-3401

    2010

    F: Maschinenbau und Mechatronik

    Zeitschriftenartikel

    D. Fisch, A. Hofmann, Bernhard Sick

    On the Versatility of Radial Basis Function Neural Networks: A Case Study in the Field of Intrusion Detection

    Information Sciences, vol. 180, no. 12, pp. 2421-2439

    2010

    Abstract anzeigen

    Classifiers based on radial basis function neural networks have a number of useful properties that can be exploited in many practical applications. Using sample data, it is possible to adjust their parameters (weights), to optimize their structure, and to select appropriate input features (attributes). Moreover, interpretable rules can be extracted from a trained classifier and input samples can be identified that cannot be classified with a sufficient degree of “certainty”. These properties support an analysis of radial basis function classifiers and allow for an adaption to “novel” kinds of input samples in a real-world application. In this article, we outline these properties and show how they can be exploited in the field of intrusion detection (detection of network-based misuse). Intrusion detection plays an increasingly important role in securing computer networks. In this case study, we first compare the classification abilities of radial basis function classifiers, multilayer perceptrons, the neuro-fuzzy system NEFCLASS, decision trees, classifying fuzzy-k-means, support vector machines, Bayesian networks, and nearest neighbor classifiers. Then, we investigate the interpretability and understandability of the best paradigms found in the previous step. We show how structure optimization and feature selection for radial basis function classifiers can be done by means of evolutionary algorithms and compare this approach to decision trees optimized using certain pruning techniques. Finally, we demonstrate that radial basis function classifiers are basically able to detect novel attack types. The many advantageous properties of radial basis function classifiers could certainly be exploited in other application fields in a similar way.

    F: Maschinenbau und Mechatronik

    Vortrag

    D. Fisch, F. Kastl, Bernhard Sick

    Novelty-Aware Attack Recognition - Intrusion Detection With Organic Computing Techniques

    3rd IFIP Conference on Biologically-Inspired Collaborative Computing (BICC 2010) at the World Computer Congress (WCC 2010), Brisbane, Australien

    2010

    F: Maschinenbau und Mechatronik

    Vortrag

    D. Fisch, M. Jänicke, Bernhard Sick, C. Müller-Schloer

    Quantitative Emergence - A Refined Approach Based on Divergence Measures

    Fourth IEEE International Conference on Self- Adaptive and Self-Organizing Systems (SASO 2010), Budapest, Ungarn

    2010

    F: Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    D. Fisch, Bernhard Sick

    Training of Radial Basis Function Classifiers With Resilient Propagation and Variational Bayesian Inference

    Proceedings of the International Joint Conference on Neural Networks (IJCNN 2009); Atlanta, GA, USA 14.-19.06.2009

    2009

    F: Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    D. Fisch, M. Jaenicke, E. Kalkowski, Bernhard Sick

    Learning by Teaching Versus Learning by Doing: Knowledge Exchange in Organic Agent Systems

    Proceedings of the IEEE Symposium on Intelligent Agents (IA 2009); Nashville, TN, USA, 30.03.-02.04.2009

    2009

    F: Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    D. Fisch, A. Hofmann, V. Hornik, I. Dedinski, Bernhard Sick

    A Framework for Large-Scale Simulation of Collaborative Intrusion Detection

    Proceedings of the IEEE Conference on Soft Computing in Industrial Applications (SMCia/08); Muroran, Japan; 25.-27.06.2008

    2008

    F: Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    A. Hofmann, D. Fisch, Bernhard Sick

    Identifying Attack Instances by Alert Clustering

    SMCia, 07, Berlin; Offenbach

    2007

    ISBN: 978-3800730469

    F: Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    D. Fisch, A. Hofmann, Bernhard Sick

    Improving Intrusion Detection Training Data by Network Traffic Variation

    SMCia, 07, Berlin; Offenbach

    2007

    ISBN: 978-3800730469