The CSE-CIC IDS datasets published in 2017 and 2018 have both attracted considerable scholarly attention towards research in intrusion detection systems. Recent work published using this dataset indicates little attention paid to the imbalance of the dataset. The study presented in this paper sets out...
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此外,攻击时间的详细信息将发布在dataset文档上。 (4)完整的交互:如图1所示,通过使用两个不同的网络和Internet通信,我们涵盖了内部LAN和内部LAN之间的交互。 (5)完全捕获:因为我们使用镜像端口,比如tap系统,所以所有的流量都被捕获并记录在存储服务器上。 (6)可用协议:提供所有通用可用协议的存在,如HTTP、HTTPS、...
Intrusion Detection Evaluation Dataset (CIC-IDS2017) 数据集 数据集简介 对自1998年以来现有的11个数据集的评估表明,大多数数据集(比如经典的KDDCUP99,NSLKDD等——就是上一个章节的数据集,笔者注)已经过时且不可靠。其中一些数据集缺乏流量多样性和容量,一些数据集没有涵盖各种已知的攻击,而另一些数据集将数据...
以前的IDS数据集都无法涵盖这11个标准。在下文中,我们简要概述了这些标准。 完整的网络配置。一个完整的网络拓扑结构包括调制解调器、防火墙、交换机、路由器和各种操作系统,如Windows、Ubuntu和Mac OS X。 完整的流量。通过拥有一个用户分析代理和受害者网络中的12台不同机器以及来自攻击网络的真实攻击。 标记的...
Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset intrusion detectionSMOTEmachine learningCSE-CIC-IDS2018imbalanced datasetIn recent years, due to the extensive use of the Internet, the number of networked ... Karatas,Gozde,Demir,... 被引量: 0发...
This repository contains an in-depth analysis of the Intrusion Detection Evaluation Dataset (CIC-IDS2017) for Intrusion Detection, showcasing the implementation and comparison of different machine learning models for binary and multi-class classification
The dataset offers an extended set of Distributed Denial of Service attacks, most of which employ some form of amplification through reflection. The dataset shares its feature set with the other CIC NIDS datasets, IDS2017, IDS2018 and DoS2017 Homepage Benchmarks Edit No benchmarks yet. Star...
NF-CSE-CIC-IDS2018-v2_cv 全量预处理生成。由源文件(https://rdm.uq.edu.au/files/ce5161d0-ef9c-11ed-827d-e762de186848)预处理生成。数据包括:n_features, e_features, edge_index,edge_label, tvt, label2idx,edge_ids,node_label b boss_c 1枚 GPL 2 分类 4 66 2024-08-13 详情 相关项...
All componenets of the jupyter-notebooks are all found in the folder. Credits Every developer deserves credit for the work and time they put in. Thank you Joule Effect for your contribution in the evaluation of Deep Learning models on NSL-KDD IDS dataset.About...