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首页> 外文会议>IEEE International Symposium on Software Reliability Engineering >Using Approximate Bayesian Computation to Empirically Test Email Malware Propagation Models Relevant to Common Intervention Actions
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Using Approximate Bayesian Computation to Empirically Test Email Malware Propagation Models Relevant to Common Intervention Actions

机译：使用近似贝叶斯计算对与常见干预措施有关的电子邮件恶意软件传播模型进行经验测试

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摘要

There are different ways for malware to spread from device to device. Some methods depend on the presence of a vulnerability that can be exploited along with some action taken by a user of the device. Malware propagating through email are one such example. While existing research has explored potential factors and models for simulating this form of propagation, it remains for these potential factors and models to be empirically tested and supported using field collected incident data. We review a common model for simulating the spread of email malware and use simulations to illustrate the potential impacts of connection topologies and different distributions of associated user actions. We use simulations to examine the potential impact of two types of commonly available interventions-patching vulnerable devices and blocking the transmission of infected messages in combination with different connection topologies and different distributions of user actions. Finally, we explore the use of Approximate Bayesian Computation (ABC) as a method to compare simulation results to empirical data to assess different model features, and to infer corresponding model parameter values from field collected email malware incident data.
机译：恶意软件在设备之间传播的方式有多种。某些方法取决于漏洞的存在，可以利用该漏洞以及设备用户采取的某些措施。通过电子邮件传播的恶意软件就是这样一个例子。尽管现有研究已经探索了用于模拟这种传播形式的潜在因素和模型，但仍需要对这些潜在因素和模型进行实地测试，并使用现场收集的事件数据进行支持。我们回顾了用于模拟电子邮件恶意软件传播的通用模型，并使用模拟来说明连接拓扑和关联用户操作的不同分布的潜在影响。我们使用模拟来检查两种常见干预措施的潜在影响-修补易受攻击的设备并结合不同的连接拓扑和不同的用户操作分布来阻止受感染消息的传输。最后，我们探索使用近似贝叶斯计算（ABC）作为一种方法，将模拟结果与经验数据进行比较，以评估不同的模型特征，并从现场收集的电子邮件恶意软件事件数据中推断出相应的模型参数值。

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