Stochastic identification of malware with dynamic traces

DOI10.1214/13-AOAS703zbMath1429.62713arXiv1404.2462OpenAlexW2032921253MaRDI QIDQ2453653

Nathan Brown, Blake Anderson, Curtis B. Storlie, Curtis Hash, Daniel Quist, Scott Vander Wiel

Publication date: 10 June 2014

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1404.2462

zbMATH Keywords

classificationsplinesempirical Bayeslogistic regressionadaptive Lassoelastic netmalware detectionrelaxed Lasso

Mathematics Subject Classification ID

Generalized linear models (logistic models) (62J12) Applications of statistics in engineering and industry; control charts (62P30)

Related Items (3)

Bayesian Models Applied to Cyber Security Anomaly Detection Problems ⋮ Stochastic identification of malware with dynamic traces ⋮ Malware Family Discovery Using Reversible Jump MCMC Sampling of Regimes

Uses Software

• reglogit

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Relaxed Lasso
• Least angle regression. (With discussion)
• Stochastic identification of malware with dynamic traces
• Simulation-based regularized logistic regression
• Logistic disease incidence models and case-control studies
• The Estimation of Choice Probabilities from Choice Based Samples
• Regularization and Variable Selection Via the Elastic Net
• Multinomial Inverse Regression for Text Analysis

This page was built for publication: Stochastic identification of malware with dynamic traces