  Recently, advances in machine learning techniques have attracted the
attention of the research community to build intrusion detection systems (IDS)
that can detect anomalies in the network traffic. Most of the research works,
however, do not differentiate among different types of attacks. This is, in
fact, necessary for appropriate countermeasures and defense against attacks. In
this paper, we investigate both detecting and categorizing anomalies rather
than just detecting, which is a common trend in the contemporary research
works. We have used a popular publicly available dataset to build and test
learning models for both detection and categorization of different attacks. To
be precise, we have used two supervised machine learning techniques, namely
linear regression (LR) and random forest (RF). We show that even if detection
is perfect, categorization can be less accurate due to similarities between
attacks. Our results demonstrate more than 99% detection accuracy and
categorization accuracy of 93.6%, with the inability to categorize some
attacks. Further, we argue that such categorization can be applied to
multi-cloud environments using the same machine learning techniques.
