There is a new cache pollution attack in the information-centric network (ICN), which fills the router cache by sending a large number of requests for nonpopular content. This attack will severely reduce the router cache hit rate. Therefore, the detection of cache pollution attacks is also an urgent problem in the current information center network. In the existing research on the problem of cache pollution detection, most of the methods of manually setting the threshold are used for cache pollution detection. The accuracy of the detection result depends on the threshold setting, and the adaptability to different network environments is weak. In order to improve the accuracy of cache pollution detection and adaptability to different network environments, this paper proposes a detection algorithm based on gradient boost decision tree (GBDT), which can obtain cache pollution detection through model learning. Method. In feature selection, the algorithm uses two features based on node status and path information as model input, which improves the accuracy of the method. This paper proves the improvement of the detection accuracy of this method through comparative experiments.

中文翻译：

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基于GBDT的信息中心网络缓存污染检测方法

在以信息为中心的网络（ICN）中出现了一种新的缓存污染攻击，它通过发送大量不受欢迎的内容请求来填充路由器缓存。这种攻击会严重降低路由器缓存命中率。因此，缓存污染攻击的检测也是当前信息中心网络亟待解决的问题。现有对缓存污染检测问题的研究中，大部分采用手动设置阈值的方法进行缓存污染检测。检测结果的准确性取决于阈值的设置，对不同网络环境的适应性较弱。为了提高缓存污染检测的准确性和对不同网络环境的适应性，本文提出了一种基于梯度提升决策树（GBDT）的检测算法，方法。在特征选择上，该算法使用基于节点状态和路径信息的两个特征作为模型输入，提高了方法的准确性。本文通过对比实验证明了该方法检测精度的提高。