Inhalt

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   1 Introduction
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   1.1 Background
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   1.2 Main Contributions
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   1.3 Structure
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  2 Fundamentals
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   2.1 Probability Density Functions
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   2.2 System classes
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   2.2.1 Continuous-state Systems
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   2.2.2 Discrete Event Systems
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  2.3 Automata classes
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   2.3.1 Languages
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   2.3.2 Untimed Automata
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   2.3.3 Timed Automata
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   2.3.4 Hybrid Automata
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  2.4 Automata Inference Algorithms
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   2.4.1 Common Elements
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   2.4.2 Learning Untimed Automata
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   2.4.3 Learning Timed Automata
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   2.4.4 Algorithm Analysis Frameworks
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   2.5 One-class Classification
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   2.5.1 One-class Classification Algorithms
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   2.5.2 Anomaly Detection
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  3 Related Work
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   3.1 Research Area Overview
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  3.2 Process Mining
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   3.2.1 Data Format
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   3.2.2 Anomaly Detection
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   3.2.3 Models & Algorithms
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   3.3 Grammatical Inference
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   3.3.1 Data Format
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   3.3.2 Anomaly Detection
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   3.3.3 Models & Algorithms
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  3.4 Sequence-based Anomaly Detection
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   3.4.1 Data Format
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   3.4.2 Anomaly Detection
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   3.4.3 Models & Algorithms
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  3.5 Other Anomaly Detection Approaches
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   3.5.1 Vector-based Anomaly Detection
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   3.5.2 Anomaly Detection on Data Streams
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   3.5.3 ATM Fraud Detection
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  4 Anomaly Detection with PDTTAs
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   4.1 Motivation
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   4.2 Probabilistic Deterministic Timed Transition Automaton (PDTTA)
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   4.3 Learning PDTTAs
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   4.3.1 PDTTA Learning Algorithm ProDTTAL
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   4.3.2 Runtime Complexity
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   4.3.3 Convergence of ProDTTAL
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   4.3.4 Properties of Timed Automata Inference Algorithms
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  4.4 Anomaly Detection
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   4.4.1 Anomaly Detection Problem
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   4.4.2 Automata-based Anomaly Detection Algorithm (AmAnDA)
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  4.5 Anomaly Detection in a Two-/Multi-class Setting
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   4.5.1 Two-class Setting
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   4.5.2 Multi-class Setting
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  5 Anomaly Detection Evaluation
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   5.1 Performance Metrics
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   5.2 The Curse of One-class Evaluation
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   5.2.1 Anomalies in Discrete Event Systems
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   5.2.2 Random anomalies
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   5.2.3 Model-based simulated anomalies
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   5.2.4 Anomaly Rate
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   5.3 Experiment Design / Scaling of Experiments
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  6 Experimental Evaluation
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   6.1 Hyperparameter Tuning
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  6.2 Experiment Setup
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   6.2.1 SMAC Setup
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   6.2.2 Default Environment
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   6.2.3 Algorithm Improvements and Implementation Details
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  6.3 Preliminary Synthetic Experiments
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   6.3.1 PDRTA Data Generation
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   6.3.2 Results
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   6.4 Synthetic Data Evaluation
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   6.4.1 Direct Anomaly Insertion
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   6.4.2 Additional Results for the Initial PDRTA
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   6.4.3 PDRTA Data Generation
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   6.4.4 PDTTA Data Generation
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   6.4.5 PNTTA Data Generation
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  6.5 Real-world Data Evaluation
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   6.5.1 Preprocessing
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   6.5.2 Experiment Setup
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   6.5.3 Results
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  6.6 Evaluation of Algorithm Scaling
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   6.6.1 Scaling Approaches
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   6.6.2 Expectations
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   6.6.3 Experiment Setup
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   6.6.4 Runtime Results
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  7 Conclusion and Future Work
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   7.1 Conclusion
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   7.2 Future Work
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  A Experimental Evaluation
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   A.1 Hyperparameter Values
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  A.2 Evaluation of Algorithm Scaling
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   A.2.1 Initial Automaton
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   A.2.2 Memory Consumption for Different Scaling Approaches
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   Acronyms
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   Notation
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   Bibliography
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  PDF  Änderung der Wahlordnung für die Wahl zum Fakultätsrat und für die Wahl des Dekanats bzw. der Dekanin oder des Dekans und der Prodekanin oder des Prodekans der Fakultät für Kulturwissenschaften an der Universität Paderborn