论文标题
使用封闭模式改善网络流量的对比度模式挖掘的可扩展性
Improving Scalability of Contrast Pattern Mining for Network Traffic Using Closed Patterns
论文作者
论文摘要
对比模式挖掘（CPM）旨在发现与目标数据集相比，其支持从背景数据集大幅增加的模式。 CPM对于表征不断发展的系统的变化特别有用，例如在网络流量分析中检测异常活动。虽然大多数现有的技术着重于提取整个对比模式（CPS）或最小集合，但有效地找到相关CPS的相关子集（尤其是在高维数据集中）的问题是一个开放的挑战。在本文中，我们专注于提取最具体的CP集，以发现两个数据集之间的重大变化。我们解决这个问题的方法使用封闭模式来大大减少冗余模式。我们对几个真实和模拟网络流量数据集的实验结果表明，我们提出的无监督算法的速度比网络流量数据上的CPM的现有方法快100倍[2]。此外，作为CPS的应用，我们证明了CPM是检测有意义的网络流量变化的高效方法。
Contrast pattern mining (CPM) aims to discover patterns whose support increases significantly from a background dataset compared to a target dataset. CPM is particularly useful for characterising changes in evolving systems, e.g., in network traffic analysis to detect unusual activity. While most existing techniques focus on extracting either the whole set of contrast patterns (CPs) or minimal sets, the problem of efficiently finding a relevant subset of CPs, especially in high dimensional datasets, is an open challenge. In this paper, we focus on extracting the most specific set of CPs to discover significant changes between two datasets. Our approach to this problem uses closed patterns to substantially reduce redundant patterns. Our experimental results on several real and emulated network traffic datasets demonstrate that our proposed unsupervised algorithm is up to 100 times faster than an existing approach for CPM on network traffic data [2]. In addition, as an application of CPs, we demonstrate that CPM is a highly effective method for detection of meaningful changes in network traffic.
