SCVIC-APT-2021

The dataset has been developed in Smart Connected Vehicles Innovation Centre (SCVIC) of the University of Ottawa in Kanata North Technology Park. In order to define a benchmark for Machine Learning (ML)-based Advanced Persistent Threat (APT) detection in the network traffic, we create a dataset named SCVIC-APT-2021, that can realistically represent the contemporary network architecture and APT characteristics.The original article where this work was initially presented is as follows: Jinxin Liu, Yu Shen, Murat Simsek, Burak Kantarci, Hussein Mouftah, Mehran Bagheri, Petar Djukic, “A New Realistic Benchmark for Advanced Persistent Threats in Network Traffic”, IEEE Networking Letters, 2022

本数据集由位于卡纳塔北科技园区的渥太华大学智能互联车辆创新中心（Smart Connected Vehicles Innovation Centre，SCVIC）研发。为构建面向网络流量的基于机器学习（Machine Learning，ML）的高级持续性威胁（Advanced Persistent Threat，APT）检测基准，我们创建了名为SCVIC-APT-2021的数据集，该数据集可真实还原当代网络架构与APT攻击的特征。本工作首次发表的原始文献如下：刘金鑫、沈宇、Murat Simsek、Burak Kantarci、Hussein Mouftah、Mehran Bagheri、Petar Djukic，"A New Realistic Benchmark for Advanced Persistent Threats in Network Traffic"，《IEEE Networking Letters》，2022年

搜集汇总

背景与挑战

背景概述

SCVIC-APT-2021是由渥太华大学开发的一个网络流量数据集，专门用于机器学习检测高级持续性威胁（APT）。该数据集包含CSV格式的训练集和测试集，适用于网络安全和入侵检测研究，并与一篇IEEE Networking Letters文章相关联。