Case Study: Understanding Internet Anomalies

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. Frequent cases of various cyber threats have been encountered over the past years. In this case study, we describe design of machine learning models used to classify Internet anomalies caused by worms, viruses, power outages, ransomware attacks, infrastructure failures, router misconfigurations, and Internet Protocol hijacks. The reported results indicate that while performance of machine learning models greatly depends on the used datasets, they are viable tools for detecting the Internet anomalies.