Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Real-world applications of Bayesian networks
Communications of the ACM
A maximum entropy approach to natural language processing
Computational Linguistics
Machine Learning - Special issue on learning with probabilistic representations
Communications of the ACM
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
The representational power of discrete bayesian networks
The Journal of Machine Learning Research
On the Determination of Subjective Probability by Choices
Management Science
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
A Case Study of Applying Boosting Naive Bayes to Claim Fraud Diagnosis
IEEE Transactions on Knowledge and Data Engineering
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Password Memorability and Security: Empirical Results
IEEE Security and Privacy
Games with Incomplete Information Played by "Bayesian" Players, I-III
Management Science
On user choice in graphical password schemes
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Analysis of distributed intrusion detection systems using Bayesian methods
PCC '02 Proceedings of the Performance, Computing, and Communications Conference, 2002. on 21st IEEE International
A maximum entropy approach to feature selection in knowledge-based authentication
Decision Support Systems
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Knowledge-based authentication (KBA) has gained prominence as a user authentication method for electronic transactions. This paper presents a Bayesian network model of KBA grounded in probabilistic reasoning and information theory. The probabilistic semantics of the model parameters naturally lead to the definitions of two key KBA metrics—guessability and memorability. The statistical modeling approach allows parameter estimation using methods such as the maximum likelihood estimator (MLE). The information-theoretic view helps to derive the closed-form solutions to estimating the guessability and guessing entropy metrics. The results related to KBA metrics and the models under different attacking strategies and factoid distributions are unified under a game-theoretic framework that yields lower and upper bounds of optimal guessability. The paper also proposes a methodology for implementing a Bayesian network-based KBA system. Further, an empirical evaluation of the relative merits of two Bayesian network structures for KBA, the Naive Bayes (NB) and the Tree Augmented Naive Bayes (TAN), confirms the hypothesis that the TAN structure is superior in terms of authentication accuracy and error rates. The results of the theoretical analysis and the empirical study provide insights into the KBA design problem and establish a foundation for future research in the KBA area.