火力与指挥控制2025，Vol.50Issue(9)：54-64,11.DOI:10.3969/j.issn.1002-0640.2025.09.007
基于N-gram频率和1D-CAN-DAT的网络入侵检测模型
Network Intrusion Detection Model Based on N-gram Frequency and 1D-CAN-DAT
摘要
Abstract
In order to solve the problem of insufficient information utilization and incomplete feature dimension in network intrusion detection,a network intrusion detection model based on N-gram frequency and 1D-CAN-DAT is proposed.The model extracts the traffic header and effective payload characteristics respectively through 1D-CAN,where the N-gram frequency is innovatively used to represent the effective payload context information.In addition,1D-DAT is introduced to construct association features and to extract deep conversation features.The experimental results show that the weighted detection accuracy of different attack types reaches 97.68%,which is improved compared with those of some existing studies.关键词
入侵检测/N-gram频率/CNN/可变形注意力机制/时间感知/TransformerKey words
intrusion detection/N-gram frequency/CNN/deformable attention mechanism/time-aware/Transformer分类
信息技术与安全科学引用本文复制引用
郑淳戈,安洋,赵利辉,孟迪..基于N-gram频率和1D-CAN-DAT的网络入侵检测模型[J].火力与指挥控制,2025,50(9):54-64,11.基金项目
山西省青年科学研究基金资助项目(202203021212114) （202203021212114）
