论文标题
基于贝叶斯模型组合的主动恶意软件分析方法
A Bayesian Model Combination-based approach to Active Malware Analysis
论文作者
论文摘要
主动恶意软件分析涉及通过执行操作来触发响应并探索多个执行路径来建模恶意软件行为。目的之一是使行动选择更有效。本文将主动的恶意软件分析视为贝叶斯的马尔可夫决策过程，并使用贝叶斯模型组合方法来培训分析仪代理。我们显示出针对其他贝叶斯和随机方法进行主动恶意软件分析的性能的改善。
Active Malware Analysis involves modeling malware behavior by executing actions to trigger responses and explore multiple execution paths. One of the aims is making the action selection more efficient. This paper treats Active Malware Analysis as a Bayes-Active Markov Decision Process and uses a Bayesian Model Combination approach to train an analyzer agent. We show an improvement in performance against other Bayesian and stochastic approaches to Active Malware Analysis.
