No abstract

Servers and users rely on safe defenses against multiple attacks. Usual practices, however, normally are unable to deal with huge distributed attacks, such as DDoS. This is a malicious practice that aims to interrupt the flow of a network causing data congestion. Moreover, DDoS is a stealthy practice, as its traffic might present similar attributes to usual ones. With this in mind, in this paper, we use unsupervised, semi-supervised, and supervised machine learning algorithms to automatically analyze a selected network, detecting possible DDoS flows using PyOD library. We evaluate each of those types of algorithms and also explore the effects of previous feature selection on them.