Machine learning algorithms have been applied to address a variety of engineering and scientific problems. The Internet has historically been prone to failures and attacks that significantly degrade its performance. To detect such anomalies, efforts are made to create datasets from data collected by initiatives such as RIPE, Route Views, and IODA. Creating these datasets involves processing large amounts of data using distributed computing techniques. The application of machine learning allows the detection of anomalies such as outages, worms, ransomware, and DDoS attacks. Machine learning algorithms can not only automate anomaly detection but also enable the detection of patterns not visible to humans due to their complexity and the volume of the number of features and data points. We will describe the entire journey from obtaining raw data through the creation of datasets to the performance results of models capable of detecting anomalies. Furthermore, essential design definitions of the Python code and models developed to carry out such scientific investigations will be addressed. Speaker(s): Luiz Felipe Silva Oliveira, , Bldg: Applied Science Bulding, 10704, Simon Fraser University, Burnaby, British Columbia, Canada, Virtual: https://events.vtools.ieee.org/m/413712