支持泛洪攻击检测的命名数据网PIT

针对命名数据网待定兴趣转发表中高效的变长名称数据索引、硬件可支持的存储消耗以及兴趣包泛洪攻击检测等问题，提出了基于字符卷积神经网络的认知索引模型（C&I），该模型能够支持路由名称数据的分类、聚合，降低名称数据的存储消耗.同时，基于C&I提出了支持兴趣包泛洪攻击检测的待定兴趣转发表（PIT）存储结构C&I-PIT及其数据检索算法，通过多级存储器部署方式，分别在片上和片下的存储器中部署索引结构及存储空间.实验结果表明，C&I-PIT在名称数据聚合、存储消耗、泛洪攻击检测等方面具有良好的性能.

Research on Pending Interest Table of Named Data Networking Supporting Interest Flooding Attack Detection

In order to solve the problems of efficient variable-length name lookup, hardware-supportable storage consumption, and detection of interest flooding attack in the pending interest table(PIT) of named data networking, an cognition and indexing model(C&I) based on character convolutional neural network is proposed. C&I can support the classification and aggregation of name data, and reduce the storage consumption of name data. At the same time, a pending interest table storage structure C&I-PIT based on C&I and its data retrieval algorithm, which supports the detection of interest flooding attack, is proposed. Through the deployment of multi-level memory, the index structure and storage space are respectively deployed on static random access memory and dynamic random access memory. Experiments show that C&I-PIT has good performance in name aggregation, memory consumption and interest flooding attack detection.