LATAM-DDoS-IoT dataset

Intrusion Detection Systems based on Artificial Intelligence need robust data sources in order to achieve strong generalization levels from the knowledge domain of interest. Anomaly detection is a well-known topic in cybersecurity, and its application to the Internet of Things can lead to suitable protection techniques against problems such as DoS and DDoS attacks. Here we present the creation of a new dataset called LATAM-DDoS-IoT, result of a collaboration between Aligo, Universidad de Antioquia, and Tecnologico de Monterrey, that includes attack traffic to physical Internet of Things devices, and normal traffic from external real users consuming real services from Aligo's production network. These characteristics make our dataset be convenient for real production environments.基于人工智能的入侵检测系统（Intrusion Detection Systems）需要鲁棒的数据源，以在目标知识领域实现优异的泛化性能。异常检测（Anomaly detection）是网络安全领域的经典研究方向，将其应用于物联网（Internet of Things, IoT）可衍生出针对拒绝服务（Denial of Service, DoS）攻击、分布式拒绝服务（Distributed Denial of Service, DDoS）攻击等网络威胁的适配防护技术。本文介绍了全新数据集LATAM-DDoS-IoT的构建工作，该数据集由Aligo、安蒂奥基亚大学（Universidad de Antioquia）与蒙特雷科技大学（Tecnologico de Monterrey）合作完成，其数据涵盖针对实体物联网设备的攻击流量，以及外部真实用户访问Aligo生产网络中实际服务所产生的正常流量。上述特性使得本数据集可高度适配真实生产环境场景。

搜集汇总

背景与挑战

背景概述

LATAM-DDoS-IoT数据集是一个专注于物联网安全的网络流量数据集，用于异常检测和入侵检测系统的研究。它包含针对物理IoT设备的DoS和DDoS攻击流量以及真实用户的正常流量，数据量超过300 GB，提供.csv、.pcap和.argus格式，适用于监督学习方法，旨在提升IoT网络的防护能力。