  One of the most common internet attacks causing significant economic losses
in recent years is the Denial of Service (DoS) flooding attack. As a
countermeasure, intrusion detection systems equipped with machine learning
classification algorithms were developed to detect anomalies in network
traffic. These classification algorithms had varying degrees of success,
depending on the type of DoS attack used. In this paper, we use an SNMP-MIB
dataset from real testbed to explore the most prominent DoS attacks and the
chances of their detection based on the classification algorithm used. The
results show that most DOS attacks used nowadays can be detected with high
accuracy using machine learning classification techniques based on features
provided by SNMP-MIB. We also conclude that of all the attacks we studied, the
Slowloris attack had the highest detection rate, on the other hand TCP-SYN had
the lowest detection rate throughout all classification techniques, despite
being one of the most used DoS attacks.
