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A Survey on Hidden Markov Model (HMM) Based Intention Prediction Techniques

机译：基于隐马尔可夫模型（HMM）的意向预测技术研究

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The extensive use of virtualization in implementing cloud infrastructure brings unrivaled security concerns for cloud tenants or customers and introduces an additional layer that itself must be completely configured and secured. Intruders can exploit the large amount of cloud resources for their attacks. This paper discusses two approaches In the first three features namely ongoing attacks, autonomic prevention actions, and risk measure are Integrated to our Autonomic Cloud Intrusion Detection Framework (ACIDF) as most of the current security technologies do not provide the essential security features for cloud systems such as early warnings about future ongoing attacks, autonomic prevention actions, and risk measure. The early warnings are signaled through a new finite State Hidden Markov prediction model that captures the interaction between the attackers and cloud assets. The risk assessment model measures the potential impact of a threat on assets given its occurrence probability. The estimated risk of each security alert is updated dynamically as the alert is correlated to prior ones. This enables the adaptive risk metric to evaluate the cloud's overall security state. The prediction system raises early warnings about potential attacks to the autonomic component, controller. Thus, the controller can take proactive corrective actions before the attacks pose a serious security risk to the system.
机译：虚拟化在实施云基础架构中的广泛使用给云租户或客户带来了无与伦比的安全问题，并引入了一层额外的层，该层本身必须完全配置和保护。入侵者可以利用大量的云资源进行攻击。本文讨论了两种方法。在前三个功能中，即持续的攻击，自主防御措施和风险衡量，已集成到我们的自主云入侵检测框架（ACIDF）中，因为大多数当前的安全技术并未提供云系统的基本安全功能。例如关于未来正在进行的攻击的预警，自主防御措施和风险衡量。预警是通过新的有限状态隐马尔可夫预测模型发出的，该模型捕获了攻击者与云资产之间的相互作用。风险评估模型根据给定的威胁发生概率来衡量威胁对资产的潜在影响。每个安全警报的估计风险会随着警报与先前警报之间的关联而动态更新。这使自适应风险度量能够评估云的总体安全状态。该预测系统会发出有关对自主组件控制器的潜在攻击的预警。因此，在攻击对系统造成严重的安全风险之前，控制器可以采取主动的纠正措施。

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