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Activity Number:
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202
- Meta-Analysis, Mediation, and Causal Inference from a Bayesian Perspective
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Type:
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Contributed
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Date/Time:
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Monday, August 8, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #323097
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Title:
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Empirical Bayesian Estimation of Malware Detection Without Knowing Ground Truth
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Author(s):
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Keying Ye* and Min Wang and Ambassador Negash and Zifei Han
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Companies:
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University of Texas at San Antonio and The University of Texas at San Antonio and University of Texas at San Antonio and University of International Business and Economics
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Keywords:
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Empirical Bayesian analysis;
malware detection;
predictive distributions
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Abstract:
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Measurements of malware detection metrics are important in the research fields of cyber security. One of the challenging problems of developing such security metrics is due to the unknown (or noisy) ground truth. In this research, we use a Bayesian framework to study the predictive distribution of malware detection. Simulation studies are carried out using synthetic data with known ground truth and accuracies in estimations are compared. Real data is also applied with the proposed approach.
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Authors who are presenting talks have a * after their name.
