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.
翻译:主动的 Maware 分析涉及通过执行触发响应和探索多个执行路径的行动来模拟恶意软件行为模型。 目标之一是提高行动选择的效率。 本文将主动的恶意分析视为一种Bayes- Avious Markov 决策程序, 并使用贝叶斯模式组合法来训练分析器。 相对于其他 Bayesian 和 Stochatic 方法, 我们显示了对主动的恶意分析的性能有所改善 。