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首页> 外文期刊>Journal of Computers >Mining Web User Behaviors to Detect Application Layer DDoS Attacks
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Mining Web User Behaviors to Detect Application Layer DDoS Attacks

机译：挖掘Web用户行为以检测应用程序层DDoS攻击

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摘要

Distributed Denial of Service (DDoS) attacks have caused continuous critical threats to the Internet services. DDoS attacks are generally conducted at the network layer. Many DDoS attack detection methods are focused on the IP and TCP layers. However, they are not suitable for detecting the application layer DDoS attacks. In this paper, we propose a scheme based on web user browsing behaviors to detect the application layer DDoS attacks (app-DDoS). A clustering method is applied to extract the access features of the web objects. Based on the access features, an extended hidden semi-Markov model is proposed to describe the browsing behaviors of web user. The deviation from the entropy of the training data set fitting to the hidden semi-Markov model can be considered as the abnormality of the observed data set. Finally experiments are conducted to demonstrate the effectiveness of our model and algorithm.
机译：分布式拒绝服务（DDoS）攻击已对Internet服务造成持续的严重威胁。 DDoS攻击通常在网络层进行。许多DDoS攻击检测方法都集中在IP和TCP层上。但是，它们不适合检测应用程序层DDoS攻击。在本文中，我们提出了一种基于Web用户浏览行为的方案来检测应用程序层DDoS攻击（app-DDoS）。应用聚类方法来提取Web对象的访问特征。基于访问特征，提出了一种扩展的隐式半马尔可夫模型来描述Web用户的浏览行为。从训练数据集的熵到隐藏的半马尔可夫模型的熵的偏差可以被认为是观测数据集的异常。最后进行实验以证明我们的模型和算法的有效性。

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