  With the growing rates of cyber-attacks and cyber espionage, the need for
better and more powerful intrusion detection systems (IDS) is even more
warranted nowadays. The basic task of an IDS is to act as the first line of
defense, in detecting attacks on the internet. As intrusion tactics from
intruders become more sophisticated and difficult to detect, researchers have
started to apply novel Machine Learning (ML) techniques to effectively detect
intruders and hence preserve internet users' information and overall trust in
the entire internet network security. Over the last decade, there has been an
explosion of research on intrusion detection techniques based on ML and Deep
Learning (DL) architectures on various cyber security-based datasets such as
the DARPA, KDDCUP'99, NSL-KDD, CAIDA, CTU-13, UNSW-NB15. In this research, we
review contemporary literature and provide a comprehensive survey of different
types of intrusion detection technique that applies Support Vector Machines
(SVMs) algorithms as a classifier. We focus only on studies that have been
evaluated on the two most widely used datasets in cybersecurity namely: the
KDDCUP'99 and the NSL-KDD datasets. We provide a summary of each method,
identifying the role of the SVMs classifier, and all other algorithms involved
in the studies. Furthermore, we present a critical review of each method, in
tabular form, highlighting the performance measures, strengths, and limitations
of each of the methods surveyed.
