TABLE OF CONTENTS ix

Transcription

1 TABLE OF CONTENTS ix

2 TABLE OF CONTENTS Page Certification Declaration Acknowledgement Research Publications Table of Contents Abbreviations List of Figures List of Tables List of Keywords Abstract i ii iii v ix xiv xviii xxi xxiii xxv CHAPTERS 1 INTRODUCTION Clustering Partitioning Methods Hierarchical Agglomerative methods The Single Link Method The Complete Link Method The Group Average Method Text Based Documents Outlier analysis Support Vector Machine Multi-Clustering Collective Animal Behavior Supervised Learning Unsupervised Learning Thesis outline 24 x

3 2 LITERATURE SURVEY Introduction Credit Card Fraud Detection using Various Methods and Techniques Credit Card Fraud Detection in Unbalanced Datasets Meta Classifier System for Credit Card Fraud Detection Role of Support Vector Machine Role of Clustering and Outlier Detection Role of Collective Animal Behaviors Summary 86 3 OBJECTIVE Summary 92 4 RESEARCH FRAMEWORK Introduction Unsupervised Approach Cost Based Support Vector Machine Approach Behavior Based Support Vector Machine Approach Hybrid Approach Summary 98 5 NOVEL APPROACH TO CREDIT CARD FRAUD DETECTION MODEL Introduction Data Pre-Processing Detection Process using Clustering Detection Process using Outliers Detection in Dense Region Detection in Sparse Region Summary 110 xi

4 6 FRAUD DETECTION IN IMBALANCED DATASETS USING COST BASED LEARNING Introduction Data Pre-Processing Detection Based on the Support Vector Machine Summary BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES Introduction Data Pre-Processing Behavior Based Approach Summary HYBRID APPROACH FOR IMPROVISING CREDIT CARD FRAUD DETECTION BASED ON COLLECTIVE ANIMAL BEHAVIOR AND SVM Introduction Pre-Processing the Dataset Multi-Clustering SVM Based Approach Collective Animal Behavior Approach Summary ANALYSIS RESULTS Unsupervised Approach Cost Based Support Vector Machine Approach Behavior Based Support Vector Machine Approach Hybrid Approach 152 xii

5 11 CONCLUSION AND FUTURE RESEARCH Summary of the Research Achievements Limitations Recommendations for Further Work 160 REFERENCES 161 CITATIONS 176 xiii

6 ABBREVIATIONS xiv

7 ABBREVIATIONS ADMP ACO AISFD AIS ANN APACS ATMs BP BBN BN B2B B2C CNP CCFD CLARA CLARANs CORM CBLOF CLINK CL C2B C2C CS-SVM CROO DEMIDS DCSA e-payment EM FP - Absolute Distance between Medoid - Ant Colony Optimization - Artificial Immune System for Fraud Detection - Artificial immune systems - Artificial Neural Network - Association of Payment and Clearing Services - Automated Teller Machines - Back-Propagation - Bayesian belief networks - Bayesian Networks - Business-to Business - Business-to-Consumer - Cardholder-Not-Present - Credit Card Fraud Detection - Clustering around LARGE Applications - Clustering Large Applications based on RAndomized Search - Cluster based OutlieR Minera - Clustering Based Local Outlier Factor - Complete Link Method - Confidence Level - Consumer-to- Business - Consumer-to-Consumer - Cost Based Support Vector Machines - Capture Resilient Online One-time password scheme - Distance Based Outlier Detection for Data Streams - Dynamic Clonal Selection Algorithm - Electronic Payment - Expectation Maximization - False Positive xv

8 FDS - Fraud Detection System FN - False Negative FP - False positive FPR - False Positive Rate FPM - Fraud Pattern Mining FPOF - Frequent Pattern Outlier Factor GESD - Generalized Extreme Studentized Deviate GA - Genetic Algorithm HMM - Hidden Markov Model HMRs - Hidden Markov Random Fields JAM - Java Agents for Metalearning k-nn - k-nearest Neighbour LCF - Local Connective Factor LOF - Local Outlier Factor MAE - Mean Absolute Error NFMS - Neural Fraud Management Systems NN - Neural Networks NRI - Non Received Issue OTP - One-Time Passwords PGNN - Parallel Granular Neural Networks PSO - Particle Swarm Optimization PAM - Partitioning Around Medoid PIN - Personal Identification Number P-RCE - Restricted Coulomb Energy PCA - Principal Component Analysis QRT - Questionnaire-Responded Transaction RBFN - Radial Basis Function Network RDA - Receptor Density Algorithm RNN - Replicator Neural Networks ROC - Receiver Operating Characteristic SOM - Self-Organizing Maps xvi

9 SLINK SVM SMOTE TN TP TPR UAVs VDM WS - Single Link Method - Support Vector Machine - Synthetic minority over-sampling Technique - True Negative - True Positive - True Positive Rate - Unmanned Air Vehicles - Value Difference Metric - Wattz-Strogatz xvii

10 LIST OF FIGURES xviii

11 LIST OF FIGURES Figure Description Page 1.1 Yearly Credit Card Frauds Inconsistent Distributions between the Training Data and Real Data Conceptual Framework for Classification of Frauds Sample Cluster Cluster 3 and Cluster 4 are Considered Outliers Artificial Linear Separable Two-Dimension Binary Classification Problem SVM with Maximal Margin Hybrid Sampling Model for Fraud Detection QRT Approach for Predicting Frauds State of Hidden Markov Model Illustration of the Predictive Nature of Flocks CCFD Framework Outline of the Fraud Detection Process Sparse and Dense Region Representation Flowchart of the Fraud Detection Process Sample Input Data for SVM Workflow of the SVM Model Difference between Over Fitting Classifier and a Better Classifier Flowchart of the Fraud Detection using SVM Data Processing in Behavior Based SVM Behavior Based Fraud Detection Using SVM Flowchart For Hybrid Approach Fraud Detection Sample Data Input Data for SVM 134 xix

12 8.4 Hybrid Approach For Improvising Fraud Detection ROC - GESD and Q-Test ROC - SVM ROC - Behavior Based SVM PR - Behavior Based SVM ROC - Hybrid Approach PR - Hybrid Approach 154 xx

13 LIST OF TABLES xxi

14 LIST OF TABLES Table Description Page 5.1 Qcrit values Confusion Matrix Sample-100 Data Sample-500 Data Sample-1000 Data TPR and FPR for GESD and Q-Test Sample Confusion Matrix Set For SVM TPR and FPR - Behavior based SVM Sample Confusion Matrix Set With TPR and FPR Precision, Recall and F-Measure Sample values 155 xxii

15 LIST OF KEYWORDS xxiii

16 LIST OF KEYWORDS Classification Clustering Collective Animal Behavior Confusion Matrix Data Mining E-commerce Fraud Detection Hybrid Approach Machine Learning Outlier Detection Supervised Learning Support Vector Machine Unsupervised Learning xxiv