SDN网络异常检测仿真数据

整体数据由西南交通大学于2020年7月产生，该成果通过检测交换机与控制器之间的交互消息，设计了一种软件定义网络（SDN）中的轻量级检测算法，经同行评议后发表于International Conference on High Performance Computing and Communications国际会议上。实验产生的数据使用Python处理绘制。支撑论文“LNAD: Towards Lightweight Network Anomaly Detection in Software-Defined Networking”。The overall dataset was generated by Southwest Jiaotong University in July 2020. This work designed a lightweight detection algorithm for Software-Defined Networking (SDN) by detecting interactive messages between switches and controllers, and was published in the International Conference on High Performance Computing and Communications after peer review. Data generated from the experiments were processed and visualized using Python. This dataset supports the paper titled "LNAD: Towards Lightweight Network Anomaly Detection in Software-Defined Networking".

搜集汇总

背景与挑战

背景概述

该数据集由西南交通大学于2020年7月产生，主要用于软件定义网络（SDN）中的轻量级异常检测算法研究，包含4个文件，数据量为292.27KB。数据集支撑了题为《LNAD: Towards Lightweight Network Anomaly Detection in Software-Defined Networking》的论文，并发表于International Conference on High Performance Computing and Communications国际会议。