  Intrusion detection system (IDS) is one of the implemented solutions against
harmful attacks. Furthermore, attackers always keep changing their tools and
techniques. However, implementing an accepted IDS system is also a challenging
task. In this paper, several experiments have been performed and evaluated to
assess various machine learning classifiers based on KDD intrusion dataset. It
succeeded to compute several performance metrics in order to evaluate the
selected classifiers. The focus was on false negative and false positive
performance metrics in order to enhance the detection rate of the intrusion
detection system. The implemented experiments demonstrated that the decision
table classifier achieved the lowest value of false negative while the random
forest classifier has achieved the highest average accuracy rate.
