JINAV Journal of Information and Visualization · 2021 · 29 citations · 22 references
ComputingComputer ScienceCybersecurityData MiningMachine LearningNetwork SecurityAnomaly DetectionDeep LearningEnsemble LearningEnsemble MethodsFraud DetectionPattern RecognitionClusteringCredit Card Fraud DetectionDeep Learning-based DetectionFraud Detection In BankingGenetic Algorithm OptimizationMachine Learning Detection ModelsModel Accuracy AssessmentModular Network Structure
Adversaries achieved such intrusion via carefully crafted attacks of large magnitude that seek to wreak havoc on network infrastructures with a focus on personal gains and rewards. Study proposes a spectral-clustering hybrid of genetic algorithm trained modular neural network to detect fraud in credit card transactions. The hybrid ensemble seeks to equip credit-card users with a system and algorithm whose knowledge will altruistically detect fraud on credit cards. Results show that the hybrid model effectively differentiates between benign and genuine credit card transactions with a model accuracy of 74%.
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Cost-based modeling for fraud and intrusion detection: results from the JAM project
Salvatore J. Stolfo, Wei Fan, Wenke Lee et al. · 2002 · 611 citations · Full text
Credit card fraud detection with a neural-network
Unsupervised Profiling Methods for Fraud Detection
Richard J. Bolton, David J. Hand · 2002 · 354 citations