计算机应用研究2024，Vol.41Issue(10)：3141-3148,8.DOI:10.19734/j.issn.1001-3695.2023.12.0633
NDP-FD6:一种 IPv6 网络NDP洪泛行为多分类检测框架
NDP-FD6:multi-classification detection framework for NDP flooding behaviors in IPv6 network
摘要
Abstract
Current researches on NDP flooding behavior detection mainly focus on detecting RA flooding and NS flooding be-haviors,and there is insufficient flooding detection for other messages of the NDP protocol.Moreover,traditional threshold rule detection methods suffer from poor dynamics and low accuracy,while most of the Al-based detection methods can only perform binary classification detection,and there are still challenges in performing multi-classification detection.In short,there is a lack of corresponding research in multi-classification flooding detection of all messages of NDP protocol.Therefore,this paper proposed a multi-classification detection framework for NDP protocol flooding behaviors,and proposed a flooding be-havior detection method for NDP protocol based on time interval characteristics.The framework constructed the first multi-classification dataset for NDP flooding detection through the processes of traffic collection and data processing,it compared and used 5 machine learning and 5 deep learning algorithms to train the detection model.The experimental results show that the detection accuracy of the XGBOOST algorithm in machine learning can reach 99.18％,and the detection accuracy of the Transformer algorithm in deep learning can reach 98.45％.Compared with the existing detection methods,the accuracy is higher.Meanwhile,the detection framework can detect 9 types of flooding behaviors for all 5 types of messages of NDP proto-col and classify the flooding behaviors into multiple types.关键词
IPv6/NDP/洪泛检测/DDoS/机器学习/深度学习Key words
IPv6/NDP/flooding detection/DDoS/machine learning/deep learning分类
信息技术与安全科学引用本文复制引用
夏文豪,张连成,郭毅,张宏涛,林斌..NDP-FD6:一种 IPv6 网络NDP洪泛行为多分类检测框架[J].计算机应用研究,2024,41(10):3141-3148,8.基金项目
河南省重点研发与推广专项(科技攻关)资助项目(232102210135,212102310989) （科技攻关）
河南省高等学校重点科研资助项目(22A520044) （22A520044）
