LATAM-DDoS-IoT dataset

Anomaly detection is a well-known topic in cybersecurity. Its application to the Internet of Things can lead to suitable protection techniques against problems such as denial of service attacks. However, Intrusion Detection Systems based on Artificial Intelligence, as a defense mechanism, need robust data sources to achieve strong generalization levels from the knowledge domain of interest. Therefore, here we present the LATAM-DDoS-IoT dataset, which results from a collaboration among Aligo, Universidad de Antioquia, and Tecnologico de Monterrey. The LATAM-DDoS-IoT dataset includes attack traffic to physical Internet of Things devices and normal traffic from real external users consuming actual services from Aligo's production network.

异常检测是网络安全领域的一项重要议题。将其应用于物联网，可衍生出针对如拒绝服务攻击等问题的一系列适宜的保护技术。然而，作为防御机制的人工智能入侵检测系统，需依托稳健的数据来源，以实现从所关注知识领域的强泛化水平。鉴于此，我们在此推出LATAM-DDoS-IoT数据集，该数据集系由Aligo、安蒂奥基亚大学和蒙特雷科技大学共同协作而成。LATAM-DDoS-IoT数据集涵盖了针对物理物联网设备的攻击流量，以及来自真实外部用户、实际消费Aligo生产网络提供的服务的正常流量。